Vehicles doi: 10.3390/vehicles8080193

Authors: Minghui Ye Meng Zhang Bowen Li Wen He Mengna Li

In light of the advancement of vehicle electrification and intelligence, four-wheel independent drive (4WID) electric vehicles (EVs) have garnered significant attention as a promising platform. Integrating advanced torque-vectoring (TV) strategies into 4WID EVs can effectively optimize the synergistic performance between handling stability and energy efficiency of the over-actuated system across various driving conditions. In this paper, a hierarchical Combined Sliding Mode Control–Adaptive Nonlinear Model Predictive Control (cSMC-ANMPC) TV strategy is proposed to enhance the comprehensive performance of 4WID EVs and ensure adaptive control across diverse driving conditions. Firstly, a hierarchical control architecture is developed to decouple the complex multi-objective problem. The upper layer performs robust stability decision-making by observing the vehicle’s state errors. The lower layer determines the optimal torque distribution throughout the powertrain. Secondly, a Combined Sliding Mode Controller (cSMC) is developed for the upper layer to promptly generate a robust stability command. By co-regulating both yaw rate and sideslip angle into a single command, it simplifies the lower layer’s task and enhances overall stability. Thirdly, a Soft Actor-Critic (SAC) intelligent tuner is integrated into the lower-layer NMPC to mitigate the effects of varying conditions on the stability–economy trade-off and strengthen the adaptability of the controller. Finally, co-simulation evaluations on the MATLAB R2023b/CarSim 2020.0platform demonstrate that the proposed cSMC-ANMPC strategy can improve comprehensive performance for the studied 4WID EV. Compared with other baselines, the stability enhancement in extreme maneuvers and the long-term energy-saving capability are remarkable, showcasing its promising performance.

Vehicles doi: 10.3390/vehicles8080192

Authors: Edgar Arturo León-Gomez Martin Espino-Garcia Ricardo A. Ramirez-Mendoza Adriana Salas-Zamarripa

This article presents a feasibility study on replacing the conventional viscoelastic materials used to mount exhaust systems to the chassis with magnetorheological materials. The objective is to reduce vibrations caused by the road irregularities that affect ride comfort through noise and vibration in the vehicle’s passenger compartment. Magnetorheological materials are already used in semi-active suspension systems to provide variable stiffness. A simple dynamic model is developed that incorporates a magnetorheological actuator, providing a simulation framework for various scenarios and operating conditions. The Bouc–Wen semi-empirical model is used to represent the magnetorheological material. Simulations were performed in accordance with ISO 8608, which defines road profiles for the different simulation scenarios. Extensive simulation tests show that the proposed approach is more effective than the viscoelastic materials commonly used in motor vehicle exhaust systems. This work advances the goal of improved noise and vibration control in vehicles and helps lay the groundwork for future research to address these problems.

Vehicles doi: 10.3390/vehicles8080191

Authors: Tobias Peichl Paul Heckelmann Stephan Rinderknecht

Connected, automated, shared, and electric (CASE) vehicle concepts are considered a promising approach for improving the sustainability of urban mobility by increasing vehicle utilization and reducing fleet size. While the energy consumption of conventional battery electric vehicles has been investigated extensively, the influence of fleet size, speed limits, and ambient temperature on the energy demand of CASE vehicle fleets has received little attention. This study presents a simulative consumption analysis of an all-electric CASE vehicle fleet based on the EDAG CityBot concept. A validated microscopic traffic simulation of the city center of Darmstadt, Germany, is coupled with a backward-facing powertrain model and detailed secondary consumer models to determine the total fleet energy consumption under varying operating conditions. The analysis considers fleet sizes between 20% and 100% of a reference fleet, together with a 17% fleet size scenario, which allows for the fulfillment of the urban mobility demand according to the vehicle system provider. Besides fleet size, three urban speed limit scenarios and five ambient temperature scenarios are evaluated. Among the investigated fleet size scenarios, the lowest mean fleet energy demand is observed at a fleet size of 20%, resulting from the opposing effects of increasing driving energy consumption and decreasing secondary consumer energy consumption. However, the difference between the 20% and 17% scenarios is not statistically significant. Furthermore, the study demonstrates that secondary consumers, particularly automated driving hardware and heating, ventilation and air conditioning systems, represent a major contribution to the total energy consumption of CASE vehicles and must therefore be considered in fleet-level energy analyses. Although an individual CASE vehicle exhibits higher average energy consumption than a conventional battery-electric vehicle, primarily due to its greater average weight and rolling resistance, an increase in utilization of more than 16% would be sufficient to offset this disadvantage.

Vehicles doi: 10.3390/vehicles8080190

Authors: Eckard Helmers Daniel Seidel Martin Weiss Yoann Bernard Vladimir Momcilovic Marko Stokic Davor Vujanovic Vera Rodrigues

This paper investigates nanoparticle emissions from older diesel passenger cars in four European countries outside of PTI (periodic technical inspection) testing facilities, using a methodology similar to that of the PTI. Per opportunistic sampling and voluntary testing, 618 diesel cars with an average mileage of 163,348 km were measured between 2020 and 2022 on the streets around university campuses and in public areas in Brussels (Belgium), Aveiro (Portugal), Belgrade (Serbia) and Birkenfeld (Germany). Through subsequent research, 109 vehicles were found not to be equipped with a diesel particulate filter (DPF) and were separated. Portable nanoparticle detectors were used to measure PN emissions in a PTI-like approach at low idle. From the 509 analysed diesel cars equipped with DPF, a high share of 31% revealed PN tailpipe concentrations of over 250 kP/cm3 and would therefore have failed the PTI established in Germany. That rate is 2–9 times higher than that found in Belgian and German PTI PN measurements, possibly caused by the fact that vehicles are serviced immediately before the PTI. Moreover, 45% of all tested diesel cars with DPF emitted concentrations higher than 20 kP/cm3. This level was found, both in the literature and in our measurements, to be indicative of a DPF operating outside its normal performance range, taking into account the measurement uncertainty. Even among relatively new vehicles with DPF (up to mileages of 180,000 km), 13% emitted as much PN as diesel cars without DPF (1000 kP/cm3 or more). In Portugal and Serbia, a higher share of cars showed elevated emissions compared to Germany and Belgium. DPF technology can in principle greatly reduce particle emissions of diesel cars. However, its efficiency degrades with time and vehicle mileage. Several factors leading to higher PN emissions have been reported; however, they cannot be discriminated in the results. This is primarily because active filter regeneration generates particle emission peaks every several 100 km, which were found to be up to four orders of magnitude higher than normal. Within the fleet on the streets, up to 4% of all diesel cars equipped with a modern DPF can be found in active regeneration mode. While at present politics and carmakers focus on electrifying the new vehicle generation, the emission problem of around 69 million diesel cars with elevated PN emissions on European streets remains unresolved so far. In European countries with a particularly high share of diesel cars, an old vehicle fleet, and a low income level, emission problems may be exacerbated.

Vehicles doi: 10.3390/vehicles8080189

Authors: Sadaf Zeeshan Muhammad Ali Ijaz Malik

Automated Guided Vehicles (AGVs) are a key part of today’s industrial automation, especially for material handling and intralogistics operations. The conventional AGV designs, such as differential-drive vehicles and fixed-steering AGVs, tend to exhibit limited maneuverability in restricted spaces. Such behavior can be attributed to comparatively large turning radii in classic designs, which limit the possibility of efficient movement. Thus, the production of affordable AGVs with high motion flexibility and load stability remains a challenge in AGV development. To resolve this issue, PID-controlled reverse-phase steering method is suggested. Experimental evaluation with 12 trials demonstrated a decreased turning radius for the designed AGV from 1.5 ± 0.08 m (literature-reported value) to 0.84 ± 0.05 m (current study finding), corresponding to an approximately 46.7% reduction. Results demonstrate the proposed AGV’s improved cornering capabilities. In addition, the lateral deviation achieved from the designed AGV stands at an average of 3.1 ± 0.5 cm, while the Root Mean Square Error (RMSE) is 3.5 cm, resulting in an overall accuracy rate of 96% ± 1.2%. Obstacle avoidance tests confirm successful performance within an obstacle range of up to 80 cm. Overall, the developed AGV represents a scalable and economical system for intelligent material handling within the industrial environment.

Vehicles doi: 10.3390/vehicles8080188

Authors: Deng Pan Bin Lu Xiaoji Zhou Yuyang Mao Lipeng Cao

Efficient discovery of safety-critical scenarios is a central problem in autonomous-driving simulation because safety-critical events are rare and the scenario parameter space is often high-dimensional. Direct random sampling can spend most of the simulation budget on samples that are weakly relevant, physically unreachable, or behaviorally inconsistent. This study proposes a constraint- and risk-driven framework for safety-critical scenario discovery. The framework first constructs a valid scenario space using physical reachability, behavioral consistency, and traffic-feasibility constraints. It then evaluates valid simulated samples with a hierarchical scenario-value model that combines kinematic criticality, longitudinal controllability risk, and scenario diversity. Finally, a risk-feedback probabilistic search updates the sampling distribution using high-value samples. The method is evaluated primarily in a lead-vehicle hard-braking scenario and additionally in a cut-in scenario. In the lead-vehicle hard-braking scenario, the proposed method improves the discovery rate from 0.68% for Random Sampling to 49.62% under a budget of 1000 evaluations. In the cut-in scenario, the discovery rate improves from 5.28% to 67.68% under the same budget. Ablation results show that constraint-aware construction reduces potentially invalid samples, while the full hierarchical scenario-value model improves coverage of discovered safety-critical samples.

Vehicles doi: 10.3390/vehicles8080187

Authors: Chagen Luo Weifu Wang Yadong Wang Di Zhang

The generation of guided trajectories for autonomous vehicles in multi-vehicle interaction scenarios remains challenging because surrounding vehicles continuously adapt their actions under uncertainty. This paper proposes a game-theoretic reinforcement learning (GT-RL) framework that combines a posterior-weighted rolling-horizon local game with proximal policy optimization (PPO) trajectory refinement. The revision makes the incomplete-information cost explicit through a normalized Bayesian posterior, a posterior expected cost, and a certainty-equivalent numerical approximation used by the SQP best-response solver. Using the supplied run-level logs (500 runs per method and scenario), GT-RL achieved mean safety scores of 94.1% (95% bootstrap CI: 93.91–94.28) in the intersection scenario and 91.5% (91.33–91.66) in the highway-merging scenario, with zero collisions in both sets of 500 runs. Paired comparisons with DQN, PPO, MPC-only, and potential-field baselines were significant after Holm correction (p < 0.001) for safety, traversal time, and comfort; DQN and PPO were faster in some cases but had lower safety and comfort. At the intersection, the rule-based method had higher safety but required 4.2 s more traversal time and had a 7.8-point lower comfort score than GT-RL. The findings support game-theoretic reasoning as a strategic prior for local interaction-aware trajectory generation. Claims are restricted to the tested low-to-moderate-speed, non-limit-handling simulations; high-fidelity vehicle dynamics and empirical large-scale timings remain areas of future study.

Vehicles doi: 10.3390/vehicles8080186

Authors: Ziyan Zhang Zhenfei Zhan Rongjie Mao Ruiyang Li Minghao Jiang Pingfan Duan

Road traffic accidents pose a significant threat to life and property. However, the multitude of contributing factors makes it challenging to pinpoint the most critical ones, thereby hindering effective prevention strategies. Therefore, this study conducts relevant work using the UK Department for Transport’s 2019 road traffic accident datasets. To investigate the distribution characteristics of accidents across various dimensions (person, vehicle, road, environment, and accident configuration), we first preprocessed the data. Missing values were imputed using a chained random forest-based multiple imputation method. To identify key contributing factors, we employed an integrated approach combining Bayesian-optimized random forest, Cramér’s V correlation test, K-modes clustering, and frequency statistics. This framework enabled the exploratory identification of potential high-risk scenarios for both non-operating and passenger vehicles. Subsequently, we applied a constraint-based Apriori algorithm to analyze correlations across these dimensions and temporal factors, revealing significant associations between accident severity and the examined attributes. Finally, a Bayesian-optimized LightGBM model was built to predict accident risk levels. External validation using the 2022 UK dataset, combined with interpretive analysis, confirmed the model’s strong generalization ability.

Vehicles doi: 10.3390/vehicles8080185

Authors: Jingjing Wang Xiayun Liu Hongli Deng Rongchuan Yu Wanting Yang

Motorcycle-dominated mixed traffic challenges vehicle-behavior modeling because motorcycles and cars differ in footprint, maneuverability, and longitudinal speed regulation. This study used roadside-video trajectories from six arterial sites in Hanoi, Vietnam. The trajectory pipeline produced 202,296 valid vehicle records, and the primary analysis included 158,821 motorcycles and 38,175 private cars. A focused manual identity-continuity audit of two 30 s clips yielded overall MOTA values of 0.9938 and 0.9977 and IDF1 values of 0.9931 and 0.9932. Motorcycles traveled faster on average but showed more stable longitudinal speed profiles: motorcycle speed standard deviation was 45% lower than the car value, and motorcycle coefficient of variation was less than half the car value. The directions of the mean-speed, speed-standard-deviation, and coefficient-of-variation contrasts were consistent across all six sites. Diagnostic reruns using lower speed bounds of 0–5 km/h and minimum trajectory lengths of 10–30 frames retained the same motorcycle–car stability direction (rrb=−0.759 to −0.643 for speed standard deviation and −0.828 to −0.728 for coefficient of variation). A three-component Gaussian mixture model was retained as a parsimonious engineering partition. Repeated-subsample stability was excellent for motorcycles (median adjusted Rand index, ARI, 0.970) and moderate for cars (median ARI 0.706), while mean maximum posterior probabilities exceeded 0.93 for both classes. Savitzky–Golay smoothing substantially increased usable acceleration coverage, but the inferred class contrast changed with the smoothing window; acceleration was therefore excluded from the primary clustering. A high-speed/high-variability rule flagged 4.62% of cars and 0.58% of motorcycles. This rule is an operational kinematic-screening heuristic, not a crash-probability model. The results provide class-specific evidence for mixed-traffic simulation calibration and cautious operational screening under the observed Hanoi arterial conditions.

Vehicles doi: 10.3390/vehicles8080184

Authors: Matee Ur Rasool Abdul Salam Muhammad I. Masud Muhammad Inam Ul Haq Zeeshan Ahmad Arfeen Mohammed Aman Farrukh Hafeez Touqeer Ahmed Jumani

Predicting the remaining useful life (RUL) of lithium-ion batteries is essential as it relates to the safety, reliability, and maintenance of both electric vehicles and energy storage systems. While machine learning algorithms have greatly enhanced the accuracy of prediction, a lot of the models available today still have some major flaws including redundant features, poor performance due to non-optimized model settings, and the necessity of the complete degradation process of the system. In this study, we develop a machine learning framework using particle swarm optimization (PSO) to perform hyperparameter tuning and feature optimization to improve lithium-ion battery RUL prediction. The framework allows for the automatic determination of the key degradation indicators, resulting in the optimization of the learning model and enhancement of both the accuracy and interpretability of the predictions. Furthermore, the framework is designed to be employed in an early-cycle learning environment wherein only the first 30% of battery discharge cycles are utilized in the model training. This method is intended to mimic the constraints of real-world applications in which complete lifecycle data is unavailable. Under a properly nested evaluation protocol, the results indicate that the model achieves a mean R2 of 0.354 ± 0.157 and RMSE of 19.13 ± 1.39 using only 30% of degradation cycles, compared to a mean R2 of 0.922 ± 0.014 and RMSE of 18.10 ± 1.11 under full-cycle training across five independent evaluations. While early-cycle prediction shows reduced and more variable explanatory power due to the smaller sample size, the model maintains reasonable error magnitude, supporting its potential utility for early diagnosis of lithium-ion batteries. The effectiveness of the framework is confirmed by feature importance analysis, evaluation of optimization convergence and multi-metric error assessment. In conclusion, the framework offers a highly interpretable and effective solution for predicting lithium-ion battery RUL, in alignment with the emerging data-driven approaches to battery management systems.

Vehicles doi: 10.3390/vehicles8080183

Authors: Mădălina Boțu Gabriel George Ursescu Ciprian Dumitru Ciofu Ioachim Mihalache Edward Rakosi

This paper presents the research, development, and functional validation of an original rear suspension system designed for hybrid reverse trike vehicles (two guided wheels on the front axle and a twin-tire-driven assembly at the rear). Conventional configurations featuring a single rear wheel exhibit severe limitations regarding lateral stability under critical dynamic regimes and induce roll-induced torsional loading in flexible chain drives. The proposed solution utilizes a twin-tire rear assembly integrated into an articulated suspension mechanism with two degrees of freedom (2 DoF), which reconfigures the geometric stability polygon from a triangle into an isosceles trapezoid. A mathematical model based on tire dynamics and tire slip phenomena demonstrates that introducing a controlled roll stiffness on the rear axle stabilizes the slip angles, ensuring a neutral and predictable steering behavior. Structural validation via finite element analysis (FEA) performed in SOLIDWORKS Simulation on the entire assembly under a conservative combined load scenario (2400 N vertical force shared by the two wheel bearings, 2400 N lateral force, and 1200 N tractive force) indicated a minimum factor of safety of 1.26 on S275N structural steel, confirmed by an eleven-run mesh independence study. Finally, the system’s functionality was experimentally confirmed through the manufacturing and road testing of a full-scale (1:1) demonstrator vehicle powered by an 1129 cc Boxer engine, highlighting a measurable increase in rollover resistance and trouble-free operation of the two-stage chain drive throughout the test program. A numerical evaluation shows that for rear-biased vehicles of the category the proposed axle raises the rollover-related lateral acceleration threshold by up to 54% and replaces the strongly oversteering balance of the single-wheel layout with a near-neutral, tunable one.

Vehicles doi: 10.3390/vehicles8080182

Authors: Victor Preu Daniel Pauer Roman Putter Peter Hecker

Adaptive restraint systems require specific occupant information, including head position, anthropometry, and safety-relevant posture states. Existing 3D human pose estimation benchmarks mostly report root-relative pose, while automotive in-cabin studies rarely evaluate these outputs across heterogeneous vehicle sensor sets. We present ICF-Fusion, a five-modality transformer fusion architecture, and evaluate it under leave-one-subject-out (LOSO) validation on the ICF-Body dataset, which includes synchronized near-infrared (NIR) camera, 60 GHz millimeter-wave (mmWave) radar, belt webbing extraction sensor (WES), seat configuration sensor (SCS), and ultra-wideband (UWB) recordings. The model localizes the head with a Mean Root Position Error (MRPE) of 6.10 cm and regresses anthropometry to mean absolute errors (MAE) of 5.36 cm for height, 3.50 cm for torso length, 1.78 cm for shoulder width, and 8.61 kg for weight. Feet-on-dashboard is detected on 9 of 10 evaluable folds without meaningful MRPE degradation. The full sensor fusion outperformed every single modality on all three tasks, but NIR alone nearly matched it for head localization and feet-on-dashboard detection. The fusion advantage was substantial only for the anthropometry estimation task.

Vehicles doi: 10.3390/vehicles8080181

Authors: Gang Li Junfeng An Junguo Si Dong Wang Wenwen Gao Yunyun Cen Hang Yu

As urban rail transit systems enter a stage of high-quality development, accurate short-term passenger flow forecasting has become essential for efficient operation and management. To improve the accuracy and robustness of multi-step short-term passenger flow forecasting under fluctuating demand conditions, this study develops a feature-enhanced Informer framework that embeds Complex Network Representation (CNR) into the Informer architecture to jointly capture both the topological characteristics of the subway network and spatiotemporal passenger flow dynamics. External factors, including subway schedules and land use around stations, are further integrated to enrich the input features. In addition, the ProbSparse self-attention mechanism is adopted to improve long-sequence dependency modeling, thereby enabling efficient multi-step passenger flow forecasting. Experiments were conducted on the Beijing metro passenger flow dataset from January to October 2024 to evaluate the proposed model. The dataset covered 264 stations and was aggregated at 15 min intervals. Based on historical passenger flow and multi-source features, the model predicts passenger flow over multiple future time steps. The overall evaluation metrics were calculated on the test set and averaged over all test samples and observed stations. The experimental results show that, compared with the standard Transformer model, the proposed model reduces the average prediction error by 16.59% on weekdays and 20.48% on weekends while maintaining stable predictive performance during peak hours. Sensitivity analysis and ablation studies are further conducted to evaluate the model performance across different station types and forecasting horizons. The results demonstrate that the proposed model can provide reliable decision support for intelligent urban rail transit operations, including transport capacity scheduling, passenger service improvement, and operating cost reduction.

Vehicles doi: 10.3390/vehicles8080180

Authors: Bhaven Naik Deogratias Eustace

Improving the safety and efficiency of transportation systems remains a global priority—both from an engineering and health perspective [...]

Vehicles doi: 10.3390/vehicles8080179

Authors: Paúl A. Montuf́ar-Paz Julio Cuisano Edison P. Abarca-Pérez Andrea V. Razo-Cifuentes Víctor D. Bravo-Morocho

Emission inventories for high-altitude Andean cities rely on sea-level certification cycles that misrepresent real-world combustion conditions. This study derives altitude-resolved emission factors (EFs) for light-duty gasoline vehicles across 0–4000 m a.s.l. in Ecuador using the purpose-built Andean Ecuador Driving Cycle (aedc), validated against naturalistic data via the Percentile Speed Vector metric (PSVm10; IGS =1.89 vs. IGS =2.30 for the WLTC). Ten vehicles (Euro III–V) were instrumented with OBD-II and portable analysers recording CO, NO, HC, and CO2 at 1 Hz over a four-year campaign (2021–2025; ≈2000 h). K-Means clustering on PSVm10 identified five operating regimes (silhouette ≈0.384). Under dynamically equivalent aedc conditions, NO, CO, and HC all peaked in the 1000–2000 m band (NO: 0.188gkm−1, 6.7× the sea-level value; CO: 4.47gkm−1, +50%; HC: 0.047gkm−1, +292%), fell in the 2000–3000 m band, and partially rebounded above 3000 m (NO: 0.186gkm−1); CO2 instead declined monotonically with altitude (182 to 119gkm−1, −35%), tracking a near-stable-to-slightly-declining fuel consumption (8.56 to 8.11L/100km) consistent with reduced aerodynamic drag at altitude partially offsetting the density penalty. These results show that altitude affects pollutants through distinct, non-monotonic mechanisms rather than a uniform trend, so that single-coefficient altitude corrections introduce systematic bias in Andean emission inventories.

Vehicles doi: 10.3390/vehicles8080178

Authors: Ralf Stetter Timo Schuchter Tobias Grüble Markus Till Julian Borowski Sven Schumacher Stephan Rudolph

Current research is investigating strategies, methods, and tools aimed at making vehicles more fault-tolerant and resilient. However, one level of product concretization—the level of abstract physics—was not yet in the focus of the scientific discourse. As a consequence of an increasing complexity of technical systems, the approaches fault-tolerant design and resilient design become even more important and decisive product characteristics concern the level of abstract physics. The paper analyses opportunities and approaches on this level, which are intended to allow an accommodation of faults and/or to increase the resilience of vehicle systems. Four different areas of potential improvement were identified. A systematic procedure and a conscious integration in industrial vehicle development processes were investigated. The research results were developed in several current vehicle component development processes and are explained using this basis. The presented research intends to open a new field of investigation, consequently a main emphasis is directed to future research contents and directions.

Vehicles doi: 10.3390/vehicles8080177

Authors: Emin Bayramov Zoltán Istenes

Motion forecasting models in the autonomous driving domain achieve high accuracy but cannot explain their predictions, creating a barrier to safety certification. This paper presents CogSig-Mamba, a model that produces causally validated temporal explanations alongside trajectory predictions. Inspired by hippocampal memory, the model follows a five-stage process: (1) synaptic tagging, where a top-k sparse gate selects which observation windows drove the prediction; (2) evidence encoding, which consolidates window content into memory representations; (3) reverse replay, which confirms causal faithfulness of tags through removal experiments; (4) spatial context, integrating road geometry for grounded predictions; and (5) constructive retrieval, which explains each predicted behavior via mode-specific attention to produce a complete Cognitive Signature. Evaluated on the Argoverse 2 dataset, CogSig-Mamba achieves minADE6 = 0.908 m and minFDE6 = 1.949 m with only 1.9M parameters. Removing tagged windows shifts predictions by 4.6 m on average, while removing untagged windows produces a negligible impact (0.46 m), confirming causal faithfulness across all 24,988 validation scenarios. To the best of the authors’ knowledge, this is the first motion forecaster with verified temporal credit assignment, supporting the audit trails envisioned by ISO 21448 for safety-of-the-intended-functionality compliance.

Vehicles doi: 10.3390/vehicles8080176

Authors: Quoc Trieu Nguyen Van Nghia Le Anh Duc Nguyen Van Hieu Nguyen Valentin Ivanov

This study investigates the influence of road-induced vertical excitation on air-gap eccentricity in in-wheel permanent magnet synchronous motors. A coupled electromechanical simulation framework is developed by integrating a field-oriented controlled PMSM model, a quarter-vehicle vertical dynamics model, and stochastic road roughness generated according to the ISO 8608. The proposed motor model is validated against experimental data obtained from a dynamometer test bench. Mixed eccentricity conditions are introduced to investigate how road excitation affects air-gap variation and motor current characteristics. A normalized torque-ripple current index is then extracted from the time-domain features of the q-axis current iq, which can be readily calculated from the phase currents and rotor position available in conventional inverter drives without requiring additional sensors. The simulation results reveal that road excitation significantly increases the fluctuation of air-gap eccentricity and amplifies torque-ripple-related current variations compared with no-road conditions. Furthermore, the proposed index increases consistently with eccentricity severity, while rougher road profiles shift the current response toward higher abnormality levels. These findings demonstrate that road–motor coupling has a significant impact on electrical fault signatures and should be considered when developing current-based condition monitoring methods for in-wheel PMSMs. The proposed framework provides a validated basis for evaluating eccentricity faults and supports the development of robust fault diagnosis techniques under realistic vehicle operating conditions.

Vehicles doi: 10.3390/vehicles8080175

Authors: Qiang Chen Jili Lin Yi Xu Yiying Chen

To address the limited maneuverability and tracking accuracy of autonomous four-wheel steering (4WS) vehicles during parallel parking in confined spaces, an adaptive linear time-varying model predictive control (LTV-MPC) strategy for integrated path tracking and steering coordination is proposed. Different from conventional MPC-based parking controllers with fixed weighting parameters and steering allocation schemes, the proposed method introduces an adaptive weighting mechanism that adjusts the tracking-error weights online according to the yaw-angle error, thereby improving the balance between tracking accuracy and control smoothness throughout the parking process. A hyperbolic tangent (tanh)-based steering allocation strategy is further developed to realize smooth transitions between reverse-phase steering and posture adjustment, while a PID compensation module is incorporated to improve steering command execution. A kinematic single-track model considering Ackermann steering geometry is established to formulate the prediction model of the controller. MATLAB/Simulink and CarSim co-simulation, together with hardware-in-the-loop (HIL) experiments, are conducted to evaluate the proposed control strategy. The experimental results demonstrate that the proposed method achieves more accurate path tracking, smoother steering responses, and higher parking stability than conventional controllers under typical parallel parking scenarios. The proposed strategy provides an effective and practical control framework for improving the low-speed maneuverability and path-tracking performance of autonomous 4WS vehicles.

Vehicles doi: 10.3390/vehicles8080174

Authors: Mohamed H. Abdelati Nawaf Mohamed Alshabibi

Last-mile delivery systems are under greater pressure to deliver cost-efficient, reliable, environmentally friendly, and resilient services amid operational challenges. Distance/cost is the usual optimization criterion for traditional vehicle routing methods, and factors related to disruptions, such as the delay frequency, delay severity, and delivery failure risk, are often treated separately or neglected. This study proposes a green-resilient last-mile delivery optimization framework that integrates operational costs, delivery delays, carbon emissions, and operational risk within a single multi-objective decision model. The proposed framework models the capacitated vehicle routing problem with time windows, accounting for vehicle capacity, service time commitments, fuel consumption, emission-level estimates, working hour limits, and lateness penalties and incorporating a disruption-based operational risk score. The risk score is based on the delay frequency, delay severity, and failure probability and can inform routing decisions based on efficiency and resilience. The framework is tested with a case study of urban last-mile delivery and compared with several benchmark scenarios: the current operational plan, a distance-based vehicle routing problem (VRP), a cost-based VRP, a green VRP, and a delay-aware vehicle routing problem with time windows (VRPTW). The results reveal balanced improvements in key performance indicators, in line with the proposed framework. It reduces the total distance by 35.11%, total operational cost by 34.01%, fuel consumption by 10.46%, CO2 emissions by 9.34%, estimated late orders by 93.45%, and total delay minutes by 80.10%, and there are no working hour violations compared to the current case. Other sensitivity, weight, and ablation analyses illustrate the trade-offs among cost/service reliability/environmental goals and risk exposures. The results show that operational risk can be incorporated into the green last-mile routing problem to facilitate more comprehensive—and thus more robust and sustainable—delivery planning in the context of disruptions in urban environments.

Vehicles doi: 10.3390/vehicles8080173

Authors: Mousa Abushattal Mohammad Nour Al-Marafi Rasha Al-Shamaseen Fadi Alhomaidat Fareh Abudawaba Ahmed Jaber

Rapid urbanization and increasing traffic density have increased the crash risk of vulnerable road users (VRUs), particularly pedestrians and cyclists. Meanwhile, the conventional statistical models have difficulties in capturing the nonlinear and complex nature of crash data, limiting their safety analysis effectiveness. This study utilized advanced Gradient Boosting machine learning and integrated it with SHapley Additive exPlanations (SHAP) using five years of crash data from Michigan, USA, employing a two-tiered modeling design consisting of a 4-class joint structure and binary subset frameworks. Rigorously evaluated using 10-fold stratified cross-validation to predict crash severity for VRUs, the CatBoost model had better predictive performance (AUC = 0.917) than LightGBM, Random Forest and the traditional Logistic Regression models. The analysis further indicated that prior crash actions, particularly risky crossing behaviors, are the most significant determinants of injury severity for both user groups. However, the pedestrian crash severity is strongly associated with lighting conditions and speed limits, while cyclist crash severity is more heavily influenced by intersection involvement and roadway geometry. Moreover, SHAP interaction analysis showed that the speed effect on severity significantly increases when it interacts with hazardous actions or poor visibility. The findings provide a critical insight into the implementation of effective measures and infrastructure improvements to increase the safety of VRUs.

Vehicles doi: 10.3390/vehicles8080172

Authors: Laurin Ludmann Jaeyoun Choi Jens Neubeck Andreas Wagner Chuchu Fan

This paper introduces Hybrid Physics-Augmented Neural Network (HyPA-Net), a hybrid modeling framework that integrates physics-based linear time-invariant models with artificial neural networks (ANNs) to address dynamic system modeling when only partial physical knowledge is available. The approach leverages the interpretability and robustness of established physical models while using ANNs—such as long short-term memory architectures—to capture unknown or nonlinear system behaviors. The methodology normalizes state and input variables for compatibility with ANN training and expands traditional recursive state-space equations for efficient backpropagation over sequences. Vehicle dynamics, specifically using a rear-wheel steering test case, validate the proposed framework. Various HyPA-Net configurations are benchmarked against pure physics-based and pure data-driven models, demonstrating improved prediction accuracy and model flexibility. The experimental results in this application confirm that hybrid models yield superior performance over strict physical approaches and can implicitly approximate submodel dynamics within a unified, yet modular, architecture, opening avenues for applications in domains where partial physics-based knowledge is available but insufficient on its own.

Vehicles doi: 10.3390/vehicles8080171

Authors: Xianren Zhou Zhongmin Wang Guangshuai Xu Yi Zheng Deguang Li Jun Lan Feiyong Long Longjie Li Dianhui Wang Huarong Liu Zebing Xu Chenggang Hao Yonghua Shi

To overcome the challenges in conventional frame structure design, namely the difficulty in balancing lightweight design and performance enhancement, the low efficiency of finite element (FE) simulation, and the tedious process of multivariable preliminary screening, an efficient optimization framework for frame structures that integrates multi-objective size optimization, machine learning-based performance prediction, and feature correlation analysis is proposed. First, for the steel–aluminum hybrid frame (with the main load-bearing components made of 6005A aluminum alloy and the critical load-bearing supports and joints made of Q345 low-alloy high-strength steel), a trade-off solution is obtained through multi-objective size optimization. Verified by FE simulation, this solution reduces the frame mass by 6.37% and increases the torsional stiffness by 10.47% compared with the initial design. At the same time, the modal performance, structural strength, and deformation control capability are all significantly improved, achieving a precise balance between lightweighting and stiffness enhancement. Second, a linear regression prediction model is constructed to achieve high-accuracy predictions. The average prediction error for torsional stiffness is only 2%, and the maximum prediction error for the seventh-order modal frequency is less than 1%. The prediction time for a single sample is less than one second, which is more than 1000 times faster than conventional FE simulation, thus efficiently replacing time-consuming simulation analyses. Finally, feature correlation analysis is adopted as an alternative to traditional sensitivity analysis. The core variables identified by this method are highly consistent with those obtained from Hypermesh sensitivity analysis, enabling rapid multivariable screening without additional simulations and greatly improving the efficiency of the preliminary analysis phase. The proposed optimization framework achieves a favorable combination of optimization effectiveness, prediction accuracy, and design efficiency. It not only provides a feasible engineering solution for the lightweight design of frame structures but also serves as a technical reference for the efficient optimization of similar complex structures, demonstrating significant engineering application value.

Vehicles doi: 10.3390/vehicles8080170

Authors: Min Duan Lian Xie Chuan Sun Junru Yang Shucai Xu Haiming Sun

Autonomous driving systems relieve drivers from continuous vehicle operation and constant monitoring, allowing them to engage in non-driving-related tasks (NDRTs). However, immersion in such tasks can impair drivers’ perception of both the takeover situation and the surrounding environment. To quantitatively assess drivers’ risk perception capability during takeover, a driving simulation platform was used to design autonomous takeover scenarios involving three types of NDRTs, three takeover request times (TOR), and two obstacle avoidance conditions. A total of forty participants were recruited to complete the driving experiment. Drivers’ eye movement data were collected, and visual metrics—including fixation, saccade, and pupil diameter—were extracted by defining areas of interest (AOIs). A subjective risk perception scale was developed and administered to measure drivers’ subjective evaluations. Together with takeover reaction time, the K-means clustering method was applied to classify drivers’ risk perception levels into three categories: high, medium, and low. The LightGBM algorithm was selected to construct a baseline classification model for assessing drivers’ risk perception levels. Subsequently, the Whale Optimization Algorithm (WOA) was employed to optimize the hyperparameters of LightGBM, resulting in the WOA-LightGBM model. This optimized model demonstrated improved recall, accuracy, precision, and F1-score, reaching 0.9210, 0.9253, 0.9261, and 0.9201, respectively. Furthermore, SHapley Additive exPlanations (SHAP) analysis was conducted to quantify the contribution of eye movement indicators to risk perception assessment. The results revealed that saccade duration in the NDRT areas significantly reduced drivers’ risk perception levels (SHAP value = −0.71), whereas increased saccade duration in the forward road area effectively restored drivers’ risk perception capability (SHAP value = 0.71). In addition, higher risk perception levels were found to enhance drivers’ takeover performance in terms of vehicle control. These findings provide valuable insights for the management of NDRTs and the optimization of autonomous vehicle takeover systems.

Vehicles doi: 10.3390/vehicles8070169

Authors: Ximeng Wu Yaheng Han Zhi Li Fang Liang Jiandong Zhu

The road surface adhesion coefficient is a key parameter affecting the dynamic control and driving safety of vehicles. Addressing the limitations of vehicle dynamics-based estimation methods under low-excitation conditions, this paper proposes a vision-perception-based CNN–Mamba hybrid regression network (CMHR-Net) for visual road friction potential estimation. By extracting road surface texture features from camera images, the proposed method predicts representative friction levels associated with different road conditions, providing prior information for vehicle active safety and control systems. First, ResNet18 is used to extract local texture features from road surface images. Second, a Mamba State Space Model is introduced to model long-distance dependencies between features, thereby enhancing global representation capabilities. Finally, the predicted adhesion coefficient value is output through a regression layer. Simultaneously, an adhesion coefficient mapping dataset based on road surface semantic attributes is constructed for model training and validation. Experimental results show that under various road surface conditions, including wet asphalt, waterlogged asphalt, waterlogged concrete, ice and snow, and joints, the proposed method significantly reduces the MAE (Mean Absolute Error) compared to the traditional CNN model. Specifically, under low adhesion conditions (μ ≈ 0.20), the error is reduced by approximately 56.9%. Furthermore, in complex variable conditions, the model significantly outperforms the traditional CNN model in both maximum error (Max Error) and mean absolute percentage error (MAPE). For example, under low adhesion conditions (μ ≈ 0.20), the MAPE decreases from 21.59% to 9.28%, and the maximum error decreases from 0.2025 to 0.0628, demonstrating superior stability and robustness. This method can achieve high-precision feedforward estimation of the adhesion coefficient without relying on vehicle dynamics excitation, providing effective support for feedforward control and active safety systems in vehicles.

Vehicles doi: 10.3390/vehicles8070168

Authors: Peng Jiang Fuyuan Wang Zhiwei Chen Wenbo Pan

Recent context-aware streaming 3D reconstruction frameworks provide a promising solution for online vehicle perception by maintaining anchor references, local pose windows, and trajectory memory. However, directly applying such frameworks to intelligent rail vehicles remains challenging because rail transit scenes are dominated by long straight motion, low-parallax visual observations, repetitive trackside structures, weak textures, and illumination variations. These characteristics may cause redundant context accumulation, unstable frame registration, and gradual trajectory drift. To address this problem, this paper proposes a motion-aware geometric context adaptation method for streaming 3D reconstruction of intelligent rail vehicles in low-parallax scenes. Instead of requiring task-specific large-scale retraining, the proposed method adapts the inference-stage geometric context using scale-normalized visual motion cues, including scale-normalized translational displacement, turning tendency, and inter-frame viewpoint variation. A motion-aware keyframe selection strategy suppresses redundant low-parallax frames while preserving geometrically informative observations in curved or pose-changing segments. An adaptive local pose reference window further regulates recent visual context to improve frame registration consistency. Experiments on rail transit sequences and the Oxford Spires dataset show that the proposed method achieves lower trajectory error than LingBot-Map and VIPE, while reducing redundant keyframe storage and preserving the qualitative continuity of rail-related structures. The method provides a practical motion-aware streaming 3D perception solution for rail transit inspection and digital infrastructure management.

Vehicles doi: 10.3390/vehicles8070167

Authors: Victor Giovanni Suntaxi Suntaxi Alexis Cordovés García Ricardo Lorenzo Ávila Rondón

Reducing aerodynamic drag on medium-duty freight trucks is essential for improving fuel efficiency; however, the relationship between local flow modification, aerodynamic loads, and propulsion-power demand has not yet been sufficiently quantified. This study evaluates the aerodynamic influence of a front deflector on a Chevrolet NQR 1015 box truck using steady RANS CFD with the k–ω SST turbulence model under zero-yaw conditions from 50 to 120 km/h. The numerical setup included near-wall inflation layers and mesh characterization, as well as grid-independence assessments based on CD, and the Grid Convergence Index. The deflector produced consistent aerodynamic improvements, reducing average drag coefficient by 14.1%, while the average lift coefficient decreased by 73.5%. These aerodynamic changes reduced the average required propulsion power from 53.86 kW to 50.39 kW, corresponding to a 6.4% reduction, with a maximum saving of 8.1% at 120 km/h. Pressure, velocity, and pressure-coefficient CP distributions indicate that the deflector promotes smoother flow redirection at the cab–box transition, attenuates suction peaks, and suggests lower pressure losses associated with the separated-flow and wake regions.

Vehicles doi: 10.3390/vehicles8070166

Authors: Yuan-Lung Lai Fu-Lung Hsu

In response to rapid shifts toward electrification, digitalization, and sustainability in the automotive industry, this study developed a sustainability-oriented, evidence-based competency framework for automotive maintenance technicians. Traditional competency frameworks, often derived from manufacturer manuals or curricula, overlook tacit knowledge from real-world maintenance practices, leading to gaps in diagnostic effectiveness, service quality, and resource efficiency. To address this limitation, 8500 maintenance records from 67 service centers (2022–2025) were subjected to quantitative content analysis to identify preliminary competency indicators across five vehicle systems. A three-round Delphi survey involving 24 senior automotive experts was subsequently conducted to validate and prioritize these indicators on the basis of mean importance scores and coefficients of variation (≤0.20). The final framework comprised 39 competencies, such as diagnostic proficiency, electronic system integration, system-level troubleshooting, and technical documentation application. Beyond traditional mechanical skills, cross-system diagnostic capability and digital tool proficiency have become essential competencies for modern electric vehicles. By transforming tacit maintenance knowledge into measurable indicators, the developed framework can contribute to supporting workforce sustainability, enhancing repair accuracy, reducing unnecessary part replacement, and improving resource efficiency. It can also inform vocational education, industry certification, and human capital development aligned with Sustainable Development Goals 8, 9, and 12.

Vehicles doi: 10.3390/vehicles8070165

Authors: Xulin Wang Jianwei Ma

Small unmanned aerial vehicles (UAVs) are widely used in civil and military fields, and their noise problem has always been the industry’s focus. Compared with a traditional propeller fan, a ducted fan offers higher aerodynamic efficiency, lower aerodynamic noise, and greater safety. It has become the key power component of small UAVs. However, due to the rigid restriction on tip clearance, the traditional integral ducted fan is prone to generating a tip leakage vortex, which produces high-intensity aerodynamic noise and significantly reduces propulsion efficiency. To address the above key problem restricting the quiet flight of small UAVs, this paper designs a segmented ducted fan (SDF). It preliminarily explores the influence of the segmented clearance on the fan’s flow field structure and acoustic radiation characteristics. Specifically, the k-ω SST (shear stress transport) turbulence model and the broadband noise source model were used to establish a computational fluid dynamics model, and the effects of fan speed (20,000–40,000 rpm) and duct spacing (0–20 mm) on its aeroacoustic characteristics were systematically studied. The results showed that the SDF’s acoustic power level maximum (APLmax) was significantly higher than that of the traditional integral structure, especially at high speed. At 40,000 rpm, increasing the duct spacing to 20 mm resulted in a sudden increase in APLmax to 194.5 dB, 61.3 dB higher than that of the integral type. Its essence was derived from the three-stage chain amplification mechanism: (1) strong tip leakage vortex induced by geometric clearance; (2) broadband noise caused by vortex impacting the duct wall; (3) resonant coupling of leakage vortex harmonic frequency and duct cavity standing wave. Based on this, a collaborative noise reduction path was proposed: compressing the spacing to ≤10 mm to suppress the intensity of leakage vortex, designing the periodicity of failure vortex combined with the serrated blade tip/inner wall rubber strip, and blocking the acoustic cavity resonance with non-uniform wall stiffness or 8–10 kHz Helmholtz resonator, providing a solution for the low-noise design of UAV propulsion system. Unfortunately, our study cannot currently resolve transient characteristics; only time-averaged velocity/pressure flow-field contours and total acoustic power distribution are obtained for qualitative analysis of macroscopic noise variation laws and flow-sound correlation.

Vehicles doi: 10.3390/vehicles8070164

Authors: Dongyang Hu Yuzhu Liang Xuexiao Feng Haiyang Huang

Overlapping bus–rail corridors between conventional bus lines and urban rail transit are common in integrated public transportation systems. When existing bus routes highly overlap with newly operated rail transit lines, redundant services may reduce resource utilization and increase operating pressure, while inappropriate route adjustment may negatively affect the travel experience of original bus passengers. To address this problem, this paper proposes a constrained passenger-time-oriented optimization model for conventional bus route adjustment under rail transit operation. The model evaluates passenger travel cost while jointly considering passenger flow demand, service continuity, route compactness, and the accessibility requirements of original bus passengers. Based on the proposed model, a bus line generation algorithm is designed to obtain feasible and compact adjustment schemes for co-linear bus segments. The methodological applicability of the proposed method is evaluated through a simulation experiment, and its practical applicability is illustrated through a real-world case study of Xiamen Bus Line 27. The results indicate that the proposed method can reduce passenger travel cost and alleviate redundant competition between conventional bus and rail transit services, while maintaining acceptable service continuity for existing passengers.

Vehicles doi: 10.3390/vehicles8070163

Authors: Lucian Miron Iulian Voicu Dan Catalin Niculescu Radu Ionescu Vlad Alexandru Ungureanu Radu Chiriac

In the current context of developing strategies to mitigate global warming driven by anthropogenic greenhouse gas emissions, hydrogen and ammonia have emerged as critical vectors for the decarbonization of the transportation, energy, and industrial sectors. Ammonia, specifically, serves as a highly effective hydrogen carrier, possessing three times the volumetric energy density of hydrogen. In this study, the authors present experimental findings from a compression ignition (CI) engine operating at a constant speed across two distinct loads. A dual-fuel strategy was employed, wherein ammonia was injected into the intake manifold to partially displace conventional diesel fuel. The results demonstrate that optimizing ammonia injection leads to a significant smoke reduction of up to 73.43% and a decrease in CO2 emissions of approximately 15.7%, albeit with a relative BTE penalty of 9.14% and an NOx increase of 9.30% at the lower load setting. These findings strongly align with earlier research, providing further evidence that ammonia effectively mitigates soot and carbon-based emissions while simultaneously reducing fuel consumption and smoke opacity.

Vehicles doi: 10.3390/vehicles8070162

Authors: Chaymae Chouiekh Ali Yahyaouy My Abdelouahed Sabri Hicham Karmouni Mudasir Ahmad Wani Kashish Ara Shakil Basma Abd El-Rahiem

Efficient traffic-state prediction at urban intersections is a critical component of intelligent transportation systems(ITS), as traffic conditions are influenced by dynamic factors such as traffic demand variability, infrastructure constraints, and operational traffic-control policies. This study proposes a deep-learning-based approach for short-term traffic-state classification using real-world traffic data collected during 2022 at the Alésia intersection in Paris. The objective is to classify traffic conditions into five operational states: Unknown, Flowing, Pre-saturated, Saturated, and Blocked. To investigate the impact of temporal modeling on traffic-state recognition, four deep learning architectures were evaluated under identical experimental conditions: Artificial Neural Networks (ANN), Simple Recurrent Neural Networks (RNN),Long Short-Term Memory networks (LSTM), and Gated Recurrent Units (GRU). Considering the highly imbalanced nature of the dataset, model performance was assessed using complementary metrics including Accuracy, Precision, Recall, F1-score, Macro-F1 score, and Balanced Accuracy. Experimental results demonstrate that recurrent architectures substantially outperform the ANN baseline, highlighting the importance of temporal dependencies in traffic-state classification. While the conventional RNN achieves high overall accuracy, its performance on minority traffic states remains limited. Among the evaluated models, the LSTM achieves the highest Balanced Accuracy (70.91%), indicating superior recognition of underrepresented traffic conditions. The GRU attains the highest overall F1-score (0.9256) and Macro-F1 score (0.497), while maintaining competitive classification accuracy (91.01%), providing the most favorable trade-off between global predictive performance and balanced class-wise recognition.The analysis of learning curves, classification reports, and confusion matrices further confirms the effectiveness of gated recurrent architectures for handling highly imbalanced multiclass traffic-state classification problems. These findings provide practical insights for the deployment of intelligent traffic-monitoring systems capable of supporting real-time traffic management and decision-making in urban environments.

Vehicles doi: 10.3390/vehicles8070161

Authors: Václav Mašek

The sideslip angle is crucial for vehicle stability, especially for single-track vehicles. As it is difficult to measure this quantity directly, the use of virtual sensors (observers) is common practice in this field.Neural network-based virtual sensors are becoming increasingly popular due to their ability to handle nonlinear conditions and noise. This paper presents a rigorous methodological approach to selecting measured quantities and determining the appropriate sampling frequency for stable sideslip reconstruction using a nonlinear autoregressive neural network with exogenous inputs (NARX-NN) model. A literature review suggests that most studies on sideslip angle estimation focus solely on achieving superior accuracy, with little extensive discussion of the selected quantities or sampling conditions required for effective estimation. This paper uses an information theory approach combined with a qualitative approach to select suitable model input quantities, the optimal number of look-ahead steps (‘embedding’), and the optimal sampling frequency to maximise the ratio between the latent information provided to the model for state reconstruction and the reduced computational burden on the electronic control unit (ECU). The results show that the sampling and computing frequency can be reduced by up to 20 times compared to the common baseline. This enables the use of less powerful hardware for the same model, resulting in better resource utilisation.

Vehicles doi: 10.3390/vehicles8070160

Authors: Kristián Čulík Alica Kalašová Miloš Poliak Peter Fabian

Passenger car unit (PCU) values are an essential input for traffic capacity assessment (TCA) of intersections, as they allow different vehicle categories to be converted into a common unit. In the Slovak Republic, current technical guidelines use fixed equivalency factors for specific intersection types. However, international research shows that PCU values depend on local traffic conditions, vehicle composition, road geometry, and vehicle interactions. Incorrectly selected factors may therefore lead to inaccurate capacity calculations and misleading conclusions regarding intersection performance. This study analyses PCU values for different vehicle categories, with a focus on heavy vehicles (HV) at roundabouts and turbo roundabouts (TR). Traffic surveys were conducted at selected intersections near industrial areas, where a higher proportion of freight traffic was expected. Manual and semi-automatic turning-movement counts were combined with high-resolution video recordings and automatic traffic counters (ATC) to obtain data on traffic volumes, vehicle composition, travel times, speeds, vehicle lengths, and time headways. The results indicate that the behavior of trucks and HV combinations may differ from the assumptions reflected in static equivalency factors. In several cases, the measured travel times and time headways did not reach the values implied by the prescribed PCU coefficients. Based on these findings, a methodology for dynamically determining PCU values was proposed. The methodology is based on the time headway principle and uses commonly available measurement devices. The proposed approach enables PCU values to be determined for either a simplified two-category vehicle classification or a more detailed classification. It may serve as an alternative to static tabulated values, particularly under non-standard traffic composition, a high proportion of HV, or specific geometric conditions of intersections.

Vehicles doi: 10.3390/vehicles8070159

Authors: Hirofumi Sugiyama Kyohei Noguchi Kei Nagasaka Idemitsu Masuda Yuta Yokoyama Shigenobu Okazawa

Full-vehicle crash simulations that account for occupant injury are essential for automobile safety assessment; however, they are computationally intensive and time-consuming. In particular, dash panel deformation plays a key role in transmitting impact loads to an occupant’s lower extremities. To address this issue, this study proposes a two-stage machine learning framework for occupant lower-limb injury assessment. In the first stage, the deformation behavior of the dash panel is predicted using a machine learning model, enabling efficient generation of a wide range of deformation patterns. In the second stage, occupant lower-limb injury metrics are evaluated based on the predicted deformation using a sled model. While the ultimate objective is to establish the complete two-stage framework, the present paper is limited to the first stage. It investigates the feasibility of machine learning-based deformation prediction. Deformation distributions of simplified structural components are predicted using an XGBoost-based machine learning model, in which principal component scores derived from geometric and deformation data serve as input features. The objective is to efficiently generate representative deformation modes from limited training data rather than optimizing prediction accuracy for individual deformation responses. Numerical experiments are conducted to investigate the effectiveness of the proposed prediction framework. The results of the proposed approach show good agreement with crash simulations in overall deformation behavior, while local deformation is not reproduced perfectly. These findings demonstrate the feasibility of machine learning-based dash panel deformation prediction as the first step toward the proposed two-stage framework for lower-limb injury assessment.

Vehicles doi: 10.3390/vehicles8070158

Authors: Tara Rajabi Nezhad Eduardo Louback Ryan Ahmed Ali Emadi

Driving simulators have become essential tools for accelerating the development of advanced driver assistance systems (ADASs) and autonomous vehicles (AVs) by enabling safe, repeatable, flexible, and cost-effective experimentation across increasing levels of vehicle automation. Despite their growing adoption in both academia and industry, the recent literature lacks a comprehensive review that captures recent advancements and the expanding role of simulators in both feature-level ADAS development and fully autonomous driving research. This paper addresses this gap by presenting a systematic review of the evolution of driving simulators and their critical contributions to automotive research, testing, and validation. A structured taxonomy of contemporary simulators is introduced, encompassing fidelity, physical configuration, scale, licensing, and system integration strategies. Key application domains are examined, including driver-centred behaviour and human–machine interaction studies, traffic modelling and control, vehicle dynamics and powertrain development, and the testing of ADAS and autonomous driving subsystems across perception, planning, control, and vehicle-to-everything (V2X) communication. This review highlights driving simulators as foundational enablers for the safe, efficient, and scalable deployment of increasingly automated vehicle technologies.

Vehicles doi: 10.3390/vehicles8070157

Authors: Wei Jiang Xuan Wang Shenggen Zhang Xiansheng Huang Jingang Liu Shuai Cao Hao Zhou Yunhan Song

The two-speed transmission system significantly enhances the powertrain matching performance of heavy-duty electric military armored vehicles by optimizing high-torque output at low speed and energy efficiency at high speed. However, most existing electric vehicles do not incorporate driving styles or real-time driving condition prediction into their shift control strategies, resulting in suboptimal gear shift timing and smoothness that fail to align with driver expectations and operational requirements. To address these limitations, this study focuses on the two-speed automated manual transmission (AMT) system in heavy-duty electric military armored vehicles. Firstly, a comprehensive shift control model is established, integrating key components such as the drive motor and power battery. Furthermore, a shift control strategy based on vehicle speed prediction and driving style recognition is proposed. The operational logic of this strategy is systematically analyzed under various driving cycles. Simulation and hardware-in-the-loop (HIL) results confirm the performance gains. Simulation and hardware-in-the-loop (HIL) results indicate that the proposed approach improves vehicle power performance by 21.36%, increases energy efficiency by 3.94%, and reduces powertrain shock by 31.81% compared to the conventional vehicle-speed-based gear shifting method. Compared to the adaptive shift schedule design method, the proposed approach reduces shifting frequency by 21.43% and improves ride comfort by at least 19.17% while maintaining comparable dynamic performance and energy efficiency.

Vehicles doi: 10.3390/vehicles8070156

Authors: Nicolae Vlad Burnete Florin Mariașiu Călin Iclodean

The automotive industry is undergoing a profound transformation driven by decarbonization objectives, digitalization, automation, connectivity, and the demand for safe and sustainable transportation systems [...]

Vehicles doi: 10.3390/vehicles8070155

Authors: Weijun Dai Changhui Liu Bo Li Jie Zhang Hongbin Wang Lihui Tang Siqi Peng Shan Zhu

As autonomous driving moves toward large-scale deployment, controllable and efficient simulation testing has become a primary means of ensuring system safety. However, in open-world environments, existing scenario catalogs often fail to cover the full spectrum of potential traffic situations, while rare yet high-risk critical scenarios are even harder to obtain. This scarcity renders traditional random sampling and parameter-sweeping strategies ineffective for identifying unknown risks. This study addresses two core challenges: (1) incomplete scenario catalogs hindering unknown critical scenario recognition and (2) insufficient critical samples, where generated scenarios struggle to balance physical realism and edge case coverage. To tackle the first challenge, we propose an open-world recognition method integrating transformers, random forests, and extreme value theory for precise unseen sample detection. Outlier and validity filtering ensure clustering reliability, and random forest activation patterns cluster unknown samples into meaningful groups to expand the scenario catalog. Experiments show the overall F1_macro improved by 2.3 percentage points over SOTA MDENet, with its clustering accuracy surpassing iterative-AutoNovel by 6.2 percentage points. For the second challenge, we introduce a reinforcement-learning-based maneuver-level generation method. It extracts maneuver semantics from trajectories, constructs a low-dimensional parameter space, and models parameter correlations via a multivariate multimodal distribution. A dual-layer LSTM agent with a composite reward iteratively optimizes policies toward high-risk edge scenarios. The results outperformed RLBE; longitudinal and lateral reconstruction errors were reduced by 32.7% and 15.3%, respectively, while high-risk time steps and the collision rate increased by 4.3% and 5.1%, respectively. Finally, we develop a CARLA-based scenario-driven simulation framework, integrating recognized and generated scenarios into closed-loop testing on high-risk road segments. CAS failure cases validate the generated scenarios’ physical feasibility and extreme challenge. Targeted augmentation of scarce critical scenarios enriches the test library and ensures broader coverage of real-world driving conditions.

Vehicles doi: 10.3390/vehicles8070154

Authors: Márton Jagicza Levente István Nagy István Lakatos

Tire pressure is a key factor influencing vehicle dynamic behavior, controllability, and handling performance. This study investigates the effect of tire pressure distribution on steady-state cornering and self-steering behavior near the handling limit. Experimental tests were performed on standardized constant-radius circular tracks at the ZalaZONE Dynamic Platform using winter and summer tires. Starting from the manufacturer-recommended reference pressure, the vehicle was tested at increasing speeds until the slip limit was approached. Symmetric and asymmetric front–rear tire pressure configurations were evaluated to assess their influence on steering demand, lateral acceleration, and handling balance. The results indicate pressure-dependent changes in steering angle demand, achievable lateral acceleration, and self-steering characteristics under the investigated test conditions. Asymmetric front–rear pressure distributions were found to modify the understeer–oversteer balance, highlighting the importance of tire pressure distribution in vehicle controllability near the handling limit. The findings provide practical trend-level insights for future studies on vehicle dynamics, stability control, and steering assistance functions, particularly in operating conditions where tire pressure may deviate from nominal values.

Vehicles doi: 10.3390/vehicles8070153

Authors: Xiaoyu Zhong Yong Liao

To address the stringent demands of intelligent connected vehicles for high bandwidth, low latency, and highly reliable communication, this paper systematically summarizes the semantic communication technology of the Internet of Vehicles (IoV) based on information “meaning” transmission, covering basic theory, key technologies, application practice and challenge and trends. First, the paper expounds the knowledge driven and task oriented paradigm characteristics of semantic communication and its efficiency advantages in the IoV. Second, in terms of key technologies, semantic extraction achieves efficient feature compression through multimodal fusion and Generative Artificial Intelligence (GAI); semantic coding employs hierarchical codebooks and adaptive strategies to optimize transmission efficiency; semantic transmission leverages deep reinforcement learning for the joint scheduling of resources such as spectrum and power; and semantic decoding utilizes reconstruction networks and GAI to enhance resilience against impairments. Application practices demonstrate that semantic communication can significantly compress image data transmission volume for autonomous driving collaborative perception while maintaining high-fidelity reconstruction under adverse channel conditions. It significantly reduces the communication load and improves the system utility in vehicle-to-infrastructure coordination and in-vehicle service. Despite facing technical challenges such as semantic consistency, dynamic adaptability, and security trustworthiness, future semantic communication will evolve towards deep integration with distributed collaborative knowledge networks, lightweight real-time decision-making agents, and integrated “communication, sensing, and computing” architectures, positioning itself as a key enabling technology for empowering Sixth Generation mobile communication (6G) of intelligent vehicular networks.

Vehicles doi: 10.3390/vehicles8070152

Authors: Yunxing Chen Guo Yu Kunhui Li Xingyu Yuan

In driver attention zone prediction tasks, accurately identifying and locating the driver’s attention zone is crucial. Traditional models have significant limitations in complex driving scenarios due to their failure to fully utilize multidimensional driving environment information. To address these issues, this paper proposes a multi-attention feature fusion network (MAFF-HRNet) for driver attention region prediction. The proposed network combines high-resolution feature extraction with bimodal RGB–semantic inputs, multi-scale feature fusion, attention-based feature refinement, and temporal modeling. The experimental results on the DR(eye)VE dataset show that MAFF-HRNet improves driver attention region prediction under the current evaluation protocol. These results indicate that semantic scene information, multi-scale spatial representation, and temporal context are beneficial for generating more accurate driver attention heatmaps in complex driving scenes.

Vehicles doi: 10.3390/vehicles8070151

Authors: Sulaiman Alfallaj Meshal Almoshaogeh Arshad Jamal Fawaz Alharbi

Traffic crash severity modeling is an important and promising aspect of road safety research. It aims to assess how key human-, vehicle-, roadway-, and environment-related factors interact to shape severity outcomes of crashes. Existing studies in this regard have predominantly relied on traditional statistical methods and simple machine learning approaches. While statistical analysis techniques are often based on unrealistic underlying assumptions, conventional machine learning models often suffer from interpretability issues. This study proposes an interpretable crash severity prediction framework that combines machine learning and deep learning models with post hoc explainability using SHAP. The research utilizes crash data from a rapidly developing region of Qassim in the Kingdom of Saudi Arabia. Crash severity was classified into three groups: fatal, injury, and property damage only (PDO). Four predictive models were developed and evaluated. These include: Random Forest (RF), Support Vector Machine (SVM), Feedforward Neural Network (FFNN), and Gradient-Boosting Machine (GBM). Various performance metrics, including accuracy, balanced accuracy, macro F1-score, and ROC–AUC, were used to assess the model. Descriptive statistical analysis showed that speeding, head-on collisions, wrong-way driving, blown-out tires, and driver fatigue are the major causes of fatal injuries. Empirical results revealed that the proposed prediction models achieved an accuracy ranging between 0.94 and 0.96 for the test data, with the RF model slightly outperforming the other models. Model interpretability analysis indicated that crash severity is significantly influenced by parameters such as crash cause, type, speed, and roadway type. The proposed framework demonstrated the effectiveness of machine learning (ML) and deep learning (DL) approaches for crash severity prediction and provides practical insights to support roadway safety interventions and policy development aimed at reducing severe and fatal crashes.

Vehicles doi: 10.3390/vehicles8070150

Authors: Vimal Singh Bisht Nikhil Kushwaha Nitin Sundriyal Sandeep Sunori Oscar Salas-Peña José Angel Barrios

An AI-based methodology was developed for estimating the state-of-health (SOH) of lithium-ion batteries based on secondary operational data and benchmarked with ANN, SVM, RF, and BiLSTM models. The proposed framework was evaluated by using tolerance-based accuracy, Bland–Altman agreement analysis, residual autocorrelation diagnostics, and Cartesian Taylor diagram comparison. The BiLSTM model was the best among the tested models for SOH prediction, with the least prediction error, best agreement with the reference SOH values, and near-white-noise residual behavior. The framework was further extended to Remaining Useful Life (RUL) prediction, where the BiLSTM model showed the most consistent overall performance. We also propose a residual-based anomaly detection as a potential extension of the battery monitoring framework. However, a quantitative evaluation of anomaly detection is out of scope in this study due to the lack of labeled anomaly data in the CALCE dataset. The proposed framework is validated by complementary statistical diagnostics, providing a robust and practical framework for non-intrusive battery health monitoring.

Vehicles doi: 10.3390/vehicles8070149

Authors: Jun Ma Zhanpeng Hu Mingyang Guo

Automotive seat pressure sensing provides a non-invasive modality for occupant state recognition and adaptive seat functions in intelligent cockpits. However, creep-induced temporal drift after seating may reduce the reliability of short-term occupant weight classification. This study analyzed 90 cushion pressure records from 30 participants, each obtained from a 20 s controlled seated trial. A single-exponential model characterized the early pressure evolution, and a reference-state mapping method compensated for temporal drift. A random forest classifier using cumulative cushion pressure features from sliding windows was adopted to compare raw, filtered, and compensated signals. A total of 76 records met the fitting quality criteria. Compared with raw signals, compensated signals increased accuracy, Macro-F1, and balanced accuracy by 13.1%, 22.7%, and 17.9%, respectively, with improved prediction consistency across windows. These results suggest that drift compensation improves temporal feature comparability and supports more stable short-term occupant weight classification under controlled seated conditions.

Vehicles doi: 10.3390/vehicles8070148

Authors: Qian Shi Haotian Li

Four-wheel-independent-drive electric vehicles are gaining increasing research attention due to their comprehensive dynamic performance. Real-time tire–road friction coefficient information contributes to the development of adaptive control algorithms and active safety control systems for such vehicles. However, traditional tire models widely adopted in existing estimation methods may fail to match practical tire characteristics accurately. Furthermore, lateral velocity serves as a critical state variable for tire–road friction coefficient estimation, whereas existing lateral velocity observers using low-cost inertial measurement unit sensors suffer from degraded estimation performance under complex driving maneuvers. To address the above challenges, this paper proposes a three-stage friction coefficient estimation framework. Firstly, vehicle lateral velocities are estimated via a piecewise gain-scheduled observer using inertial measurement unit measurements. Secondly, tire slip ratios are calculated based on the observed lateral velocities; meanwhile, the longitudinal, lateral and vertical forces of each tire are reconstructed. Lastly, tire force and slip information under combined slip conditions are acquired, and a multilayer perceptron neural network is established to achieve individual tire–road friction coefficient estimation. The simulation results verify the numerical feasibility and preliminary effectiveness of the proposed estimation method under ideal simulation conditions.

Vehicles doi: 10.3390/vehicles8070147

Authors: Mei Cao Qingman Fan

To address the issues of slow response to preceding vehicles and poor string stability in distributed platoon control of connected and autonomous vehicles (CAVs) under mixed traffic flow, this paper proposes a sliding mode control method based on LSTM trajectory prediction, denoted as LSTM-SMC, within a multi-agent framework. The LSTM model is trained using the HighD naturalistic driving dataset to achieve high-precision prediction of the leader vehicle’s trajectory over a horizon of 3 s, with root mean square errors (RMSE) of 8.52 m in the X-direction and 0.896 m in the Y-direction. The predicted trajectory information is converted into a preview error and embedded directly into the design of the sliding surface, enabling each following vehicle to anticipate disturbances before they propagate. A diminishing preview gain strategy (γ1=0.4, γ2=0.2, γ3=0.1) is employed to suppress error propagation along the platoon, while a saturation function is introduced to eliminate chattering and ensure smooth control inputs. Three simulation scenarios—prescribed leading, HDV (human-driven vehicle) leading, and curved road scenario—are constructed to validate the proposed method against traditional constant time headway (CTH) control, pure sliding mode control (SMC), and LSTM-MPC. Results demonstrate that under extreme conditions, the proposed method reduces the speed RMSE of the 3rd following vehicle by 18.3% compared to CTH and by 39.7% compared to SMC. Under HDV leading conditions, all string stability amplification factors are less than 1, and the position RMSE of the 3rd vehicle is only 5.03 m in the curved road scenario. Compared with LSTM-MPC, the proposed LSTM-SMC achieves comparable tracking accuracy while reducing computational cost by 1.43–3.51×. The proposed method achieves a native integration of prediction and robust control, significantly improving tracking accuracy, string stability, and computational efficiency across diverse operating conditions in mixed traffic flow.

Vehicles doi: 10.3390/vehicles8070146

Authors: Shanmukha Srivathsav Satujoda Kevin Huggins

Optimizing a high-fidelity GT3 race car setup is a serious dimensional, nonlinear problem in which small changes to mechanical parameters can affect lap time, handling balance, and vehicle stability. Existing motorsport AI studies largely emphasize racing line optimization, autonomous control, race strategy, or offline vehicle dynamics estimation, while the mechanical setup layer is often treated as fixed or tuned manually. This paper presents a staged simulator-based setup optimization framework augmented with setup-conditioned telemetry response modeling. Using the virtual BMW Z4 GT3 vehicle model implemented within the Assetto Corsa (v1.16.4) simulation environment as a controlled GT3 test platform, 134 setup configurations were evaluated at the Red Bull Ring under a fixed simulator AI driving policy. The staged search improved the best lap time from 91.430 s to 91.040 s, corresponding to a 0.390 s reduction. To move beyond a single aggregate lap-time claim, the full telemetry corpus was processed into 585 stable laps and 29,250 track-position segment samples. A setup-conditioned LightGBM model was trained to predict segment time and local vehicle response metrics from setup parameters and segment context, using five-fold GroupKFold validation by telemetry file to avoid random row leakage. The setup-conditioned segment model reconstructed held-out file-level lap time with 0.223 s mean absolute error and Spearman correlation of 0.961, outperforming a setup-only model at 0.288 s, a track-only segment model at 0.687 s, and a shuffled-setup placebo at 0.776 s. The same setup-conditioned model also improved the prediction of segment-level speed, slip angle, tire load spread, rake (defined here as rear-front ride height difference), tire temperature, yaw response, and lateral acceleration. These results show that high-frequency telemetry can support not only staged setup search, but also quantifiable learning of where and how setup changes alter vehicle behavior around the lap.

Vehicles doi: 10.3390/vehicles8070145

Authors: Eva Michelaraki George Yannis

The rapid evolution of transportation technologies and the growing integration of artificial intelligence (AI) are transforming the landscape of road safety and smart mobility [...]

Vehicles doi: 10.3390/vehicles8070144

Authors: Saeed Shirazi Majid Ghassemi Mahmoud Chizari

Decarbonizing heavy-duty logistics requires powertrains that integrate novel topology design, degradation-aware optimization, and robust dynamic performance under real-world operational loads. While solid oxide fuel cells offer high efficiency, their application in transportation is hindered by thermal fatigue. This study proposes a novel hybrid powertrain topology integrating a metal-supported solid oxide fuel cell (SOFC), a micro gas turbine (MGT), and an aluminum–silicon phase change material (PCM) thermal buffer. A high-fidelity dynamic model is developed and coupled with a multi-objective optimization framework to size the PCM buffer and battery pack, balancing capital expenditure and system lifetime. Furthermore, a degradation-aware energy management strategy based on a thermal state-of-charge metric is introduced. Simulations over a 10 h dynamic drive cycle indicate that the optimal configuration (120 kg PCM, 80 kWh battery) extends the SOFC’s simulated remaining useful life to 38,400 h, a 2.5-fold improvement over unbuffered systems. Concurrently, the proposed energy management strategy reduces the MGT mechanical wear index by 98% compared to conventional load-following strategies. The system demonstrates robust performance across ambient temperatures from −20 °C to +45 °C and achieves a 22% reduction in projected capital expenditure compared to standard proton exchange membrane fuel cell powertrains. This topology offers a highly durable and economically viable pathway for next-generation zero-emission heavy-duty vehicles. This work addresses a critical gap in the literature: the lack of integrated thermal buffering and degradation-aware control strategies for high-temperature fuel cell systems in dynamic vehicular applications. By coupling a physical latent heat buffer with a novel Thermal-SOC-proportional Energy Management Strategy, the proposed architecture directly targets the primary degradation mechanisms that have historically impeded SOFC commercialization in heavy-duty transport.

Vehicles doi: 10.3390/vehicles8070143

Authors: Jia Wang Hu Huang

Foggy traffic scenes pose significant challenges for object detection because reduced contrast, blurred object boundaries, and the loss of local details weaken discriminative feature representations. These degradations are particularly detrimental to lightweight detectors used in intelligent transportation and vehicle perception systems, where both accuracy and real-time efficiency are required. To address this problem, this paper proposes YOLO-GCM, a lightweight detector-side feature enhancement framework built upon YOLO11n. Instead of relying on an external image dehazing stage, YOLO-GCM improves the internal feature representation of the detector through three complementary modules: a gated additive feature block (GAFB) for adaptive channel-wise feature selection and noise suppression, a context-aware feature enhancement module (CAFEM) for strengthening high-level semantic context, and a multi-scale adaptive fusion (MSAF) module for enhancing cross-scale feature interaction. By integrating these modules into a unified one-stage detector, the proposed method improves detection robustness under low-visibility traffic conditions while maintaining a compact architecture. Experiments on the FoggyCar dataset show that YOLO-GCM achieved 89.81% mAP@0.5 and 67.99% mAP@0.5:0.95, outperforming standard YOLO baselines and dehazing-assisted detection pipelines under a consistent evaluation protocol. Additional evaluation on Foggy Cityscapes further verified the generalization capability of the proposed method under domain shift. The results demonstrate that detector-side feature enhancement provides an effective and efficient alternative to multi-stage dehazing-plus-detection pipelines for foggy traffic object detection. These findings can provide useful guidance for the development of robust and efficient perception modules in roadside monitoring, intelligent transportation systems, and vehicle-assisted driving applications under adverse weather conditions.

Vehicles doi: 10.3390/vehicles8070142

Authors: Zoltán Szávicza Dániel Pup Péter Raffai Zsolt Maldrik

The transport sector plays a significant role in air pollution, and real-world emissions measurements are becoming increasingly important. In this study, emissions from a turbocharged, direct-injection gasoline internal combustion engine (ICE) vehicle and a port fuel injection (PFI) hybrid electric vehicle (HEV) were compared using a portable emissions measurement system (PEMS) under real-world driving conditions. The CO2, CO, NOx, and PN emissions of the two vehicles were measured in urban, rural, and motorway sections. HEV CO2 emissions were ~20% lower than ICE emissions in the entire Real Driving Emissions (RDE) cycle, while in urban operation, they were almost 50% lower. PN emissions were lower for HEV in rural and motorway sections than for ICE, but significant PN peaks occurred during the early urban phase, attributable to the slower engine warm-up of the HEV. Machine learning analysis (Random Forest and Extra Trees Regressor) indicated that coolant temperature was the dominant driver of HEV PN emissions. The results indicate that powertrain characteristics and thermal management strongly influence real-world driving emissions, highlighting their importance for the further development of hybrid vehicles.

Vehicles doi: 10.3390/vehicles8070141

Authors: Shunpei Kawaguchi Toshiya Arakawa

Unexpected cut-in events may elicit driver anger even when braking is partly supported by driver-assistance systems. This preliminary simulator study examined whether SAE Level 1 longitudinal braking assistance alters affective responses to dangerous cut-in events. Ten young male licensed drivers completed three within-subject scenarios: manual driving without a cut-in, manual driving with a dangerous cut-in, and SAE Level 1 braking assistance with a dangerous cut-in. STAXI State Anger and salivary amylase were measured before and after each scenario. STAXI State Anger showed an overall scenario effect (p = 0.0045), but Holm-corrected post hoc comparisons were not statistically significant. In particular, the data did not indicate an anger-reducing effect of braking assistance compared with manual driving during the same cut-in event. Salivary amylase showed no significant scenario effect (p = 0.273). These preliminary findings suggest that physical braking assistance alone may be insufficient to mitigate anger-related responses to sudden cut-in events, and they motivate future controlled studies of cognitive support and system intent communication in ADAS contexts.

Vehicles doi: 10.3390/vehicles8060140

Authors: Waleed Faris

Automotive Noise, Vibration, and Harshness (NVH) has emerged as a critical interdisciplinary field influencing vehicle performance, passenger comfort, brand perception, and regulatory compliance. This thematic literature review synthesizes key research trends, methodological approaches, and technological developments shaping contemporary NVH studies. Drawing on 255 scholarly and industry sources, the review identifies five dominant themes: (1) sources and characterization of noise and vibration in internal combustion, hybrid, and electric vehicles; (2) advanced modeling and simulation techniques—including finite element analysis, statistical energy analysis, and machine learning–based prediction models; (3) materials, components, and structural optimization strategies for NVH mitigation; (4) the rapidly evolving landscape of electric and autonomous vehicle NVH; and (5) emerging active noise and vibration control technologies and data-driven diagnostics. The analysis highlights a definite shift toward holistic, data-driven, and multi-physics approaches, driven by lightweighting imperatives, widespread electrification, and increasingly stringent occupant comfort expectations. Key gaps in current research—including the need for unified evaluation metrics, real-time in-vehicle NVH monitoring, closer integration of subjective psychoacoustic perception with objective physical measurement, and validated simulation workflows for novel EV architectures—are identified and discussed. This review provides a consolidated and expanded framework for understanding contemporary NVH research directions and articulates opportunities for transformative innovation in next-generation vehicle development.

Vehicles doi: 10.3390/vehicles8060139

Authors: Thaar Alqahtani Fawzan Alfawzan

Saudi Arabia currently lacks a nationally representative, multi-day National Household Travel Survey comparable to the US, UK, or New Zealand programmes; existing official data products focus on aggregate road-transport indicators or general household statistics rather than detailed day-to-day travel diaries. This study develops a benchmark-driven framework for NHTS–KSA by comparing Saudi demographic, geographic, infrastructure, climate, and mobility indicators with those of the United States, United Kingdom, and New Zealand, and by systematically assessing 15 survey-design indicators across their national household travel surveys. Context benchmarking identifies the United States as the closest for highway-oriented interurban structure and motorisation level, New Zealand for geography and demographic structure (in particular, near-identical physiological density on limited arable land), and the United Kingdom as the most aspirationally aligned benchmark for the multimodal mobility patterns Saudi Arabia aims to develop under Vision 2030. Design benchmarking shows that the three surveys are closely matched in aggregate similarity but lead on distinct elements: New Zealand on diary length and integrated passive tracking, the US on digital tools and emerging-behaviour modules, and the UK on interviewer-led recruitment and multimodal analysis, a pattern that proves robust to plausible variation in individual scores. The resulting NHTS–KSA blueprint specifies a statistically justified, stratified multistage annual household sample, a two-day diary with rolling 12-month fieldwork, interviewer-assisted recruitment, a digital-first diary with optional GPS tracking, and modules on long-distance travel, telework, e-commerce, gendered mobility, accessibility, safety, and environmental attitudes. While preserving international comparability, the framework provides the data foundation required to steer public-transport investment, demand-management measures, and land-use policies in line with Saudi Arabia’s Vision 2030 objectives for sustainable, inclusive, and smart mobility.

Vehicles doi: 10.3390/vehicles8060138

Authors: Regina Deisling Robert Dehnert Christian Koch Melanie Schmaltz Bernhard Schaaf-Christmann Jan Messerschmidt Ramiz Dilji Bernd Tibken

The electrification of public transportation requires reliable and efficient technologies for energy transfer. Trolleybus systems represent a promising solution, as they combine high energy efficiency with reduced battery requirements. However, a central technical challenge is the precise and automatic positioning of the flexible current collector poles that connect to the overhead line. During positioning through motor actuation, the current collector shoe is caused to oscillate by external disturbances and the movement itself. To reduce oscillations, the current collectors need to be damped actively by respective actuation. This task critically depends on accurate and fast motor speed estimation for real-time control of the actuating motors. Since motor speed is not measured directly in the system, it has to be estimated from the encoder-based motor position, which introduces sensitivity to measurement noise and requires filtering. This work investigates four practical estimation approaches in the context of trolleybus applications. These include discrete-time numerical differentiation combined with FIR and IIR filtering and a modern algebraic differentiation approach. These estimation methods are evaluated under identical experimental conditions and predefined filter specifications focusing on noise suppression and time delay characteristics. The most promising approaches are further validated in closed-loop operation with respect to measurement noise-induced variations in the control input and motor speed tracking accuracy. The results demonstrate that algebraic differentiation achieves a favorable balance between noise suppression, latency, and filter order for the considered current collector system. It therefore provides a suitable basis for real-time deployment in the investigated current collector positioning control and for future active oscillation damping strategies.

Vehicles doi: 10.3390/vehicles8060137

Authors: Zeynab Rokhi Ali Emadi

The growth of Intelligent Transportation Systems (ITS) has made high-quality perception data from multi-sensor setups essential. Pairing millimeter-wave (mmW) radar with a monocular camera is a common way to recover three-dimensional information about the environment, but aligning the two is difficult because sparse radar point clouds and dense camera images differ sharply in how they sense a scene. The problem grows more severe in roadside infrastructure, where the high mounting elevation introduces perspective distortion that vehicle-mounted systems rarely face. This paper presents a systematic review, conducted under the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, of radar-camera extrinsic calibration for fixed roadside infrastructure, organizing existing work into a taxonomy that separates traditional two-stage pipelines from recent end-to-end learning frameworks. Because methods designed specifically for roadside units remain scarce, the review also covers vehicle- and robot-mounted methods whose static-sensor formulation carries over to fixed roadside deployment. For the two-stage pipeline, the analysis covers target-based and targetless correspondence registration along with the optimization techniques and algorithmic assumptions behind parameter estimation. The end-to-end learning literature shows a clear shift toward self-supervised and fusion-based models, some of which report real-time performance. The review also compares the metrics and procedures used to quantify calibration accuracy. Progress is evident, but robustness in cluttered urban environments remains an open challenge, and the paper closes by outlining future directions, arguing that standardized roadside benchmarks are needed before scalable, targetless calibration can mature.

Vehicles doi: 10.3390/vehicles8060136

Authors: Nick Barua Masahito Hitosugi

Introduction: Pedestrians lying on the road—collapsed through medical emergency, intoxication, or displacement following a prior collision—represent a disproportionately lethal and underaddressed category in road traffic safety. Forensic database analyses derived from Japan’s national police records document a fatality rate of 33.0% for collisions involving pedestrians lying on the road, more than double the rate for upright pedestrian collisions. Standard Advanced Driver-Assistance Systems (ADAS) yield a True Positive Rate (TPR) of only 21.4% for detecting pedestrians lying on the road under night conditions—a classification gap of 73.3 percentage points. Methods: In simulation trials, we evaluated the Advanced Falling Object Detection System (AFODS—where “falling object” denotes the low-profile human form at road level, distinguishing the prone pedestrian from the upright postures addressed by conventional ADAS) on a composite dataset of 3200 annotated fall events and 12,000 negative samples (training/validation), with 320 independent controlled simulation trials used for performance evaluation, spanning real-world, forensic-reconstruction, and Total Human Body Model for Safety (THUMS)-validated synthetic scenarios. No physical prototype has been evaluated; all performance data are derived from simulation, and 37.5% of positive samples are synthetically generated. These simulation conditions represent a first feasibility demonstration pending real-world hardware validation. This paper introduces three original contributions absent from prior work: a three-stage quantitative injury-risk model, a formal ISO 26262 Hazard Analysis and Risk Assessment (HARA), and a medicolegal SHAP interpretability framework. The injury-risk model translated detection latency via impact velocity to Head Injury Criterion (HIC) and estimated fatal injury probability (AIS ≥ 5); these model outputs should be interpreted as exploratory estimates pending ATD validation. Reporting follows principles consistent with the TRIPOD statement. Results: Under clear daytime conditions, AFODS demonstrated a TPR of 98.2% (95% CI: 97.4–98.8%) in simulation, decreasing to 95.6% under night dry-road conditions and 89.4% under night rain. The system achieved an AUC of 0.981 and a mean end-to-end latency of 46.5 ms, representing a 76.8 percentage-point improvement in simulation over the monocular RGB baseline (p < 0.001). The injury-risk model projects a reduction in estimated fatal head injury probability from 66.2% (Monte Carlo mean) (no detection, 50 km/h full-speed impact) to 0.7% under AFODS worst-case night/rain conditions, and to ≈0% under clear daytime simulation conditions. Conclusions: A 73.3 percentage-point classification gap places pedestrians lying on the road outside the effective detection envelope of current ADAS, compounded by the systematic exclusion of non-upright postures from regulatory test protocols and benchmark datasets. AFODS supports proof-of-concept feasibility under simulation conditions. Three translational steps are required: prototype validation on real-world hardware using instrumented Anthropomorphic Test Devices (ATDs); prone-posture biomechanical injury modelling using HIC and BrIC criteria; and regulatory extension of pedestrian AEB test standards to non-upright scenarios.

Vehicles doi: 10.3390/vehicles8060135

Authors: Daria Sachelarie Andrei Ionut Dontu Adrian Sachelarie Aristotel Popescu Lamara Achitei George Achitei

This paper investigates the potential of localized air-curtain microclimate control to reduce HVAC energy consumption in electric vehicles while maintaining occupant thermal comfort. The study compares conventional full-cabin cooling with driver-focused and passenger-focused air-curtain configurations under controlled ambient conditions of 32 °C. The experimental framework combines analytical airflow and heat-transfer modeling with comparative HVAC performance evaluation using power consumption, time to reach thermal comfort, and Predicted Mean Vote (PMV) analysis. The results show that the air-curtain configurations reduce HVAC power consumption from 3.2 kW for conventional cooling to 2.3 kW and 2.5 kW for the driver- and passenger-focused configurations, corresponding to energy savings of approximately 22–28%. In addition, localized airflow significantly accelerates thermal comfort attainment, reducing stabilization time from 8 min to 4–5 min while maintaining PMV values within acceptable comfort limits. The findings demonstrate that occupant-centered air-curtain microclimate strategies can improve HVAC energy efficiency, reduce auxiliary energy demand, and support more sustainable and range-efficient operation of next-generation electric vehicles.

Vehicles doi: 10.3390/vehicles8060134

Authors: Dequan Zeng Lingang Yang Min Xiong Akos Odry Larisa Rybak Dmitry Malyshev Jiawen Sun Yiming Hu Jinwen Yang

The reliability of the steering system directly impacts the safety of autonomous driving. Addressing the issue of trajectory deviation easily caused by motor failure in redundant steer-by-wire (SBW) systems, this paper aims to improve vehicle tracking accuracy under fault conditions. A hierarchical fault-tolerant control strategy based on a zeroing neural network (ZNN) is proposed: the upper layer uses the Stanley algorithm for path planning, while the lower layer designs a ZNN controller with preset performance constraints, and instantaneous power reconfiguration is achieved through Jacobi pseudo-inverse. Simulation results show that under high-speed lane changes and sinusoidal conditions, this strategy can achieve millisecond-level task reassignment, and compared to PID control, the maximum absolute error of lateral tracking under fault conditions is reduced by over 50%, and the root mean square error is reduced by over 30%. This method effectively improves driving safety and trajectory fidelity when actuators fail.

Vehicles doi: 10.3390/vehicles8060133

Authors: Jiwei Zhang Ziting Huang Liang Li Jun Zeng Hui Yuan Changcheng Yin

As a core component for restraining cab roll, the lateral stabilizer bar bears continuous complex alternating loads during vehicle operation, making it highly susceptible to fatigue failure that may trigger severe traffic accidents. Therefore, fatigue analysis of the lateral stabilizer bar is of great significance. To address the drawbacks of conventional direct load testing, such as difficult sensor arrangement and long test cycles, this paper proposes a fatigue-load decomposition and life evaluation method, combining multi-body dynamics and virtual iteration. Firstly, target signal spectra of the frame are obtained via real-vehicle road tests, and a high-precision system dynamic model is established with key suspension parameters. Subsequently, virtual iteration technology is adopted to accurately inverse-solve load spectra at critical points of the lateral stabilizer bar. Finally, the finite element model of the lateral stabilizer bar is validated through modal tests, and the fatigue life and vulnerable regions of the lateral stabilizer bar are predicted using the material S-N curve. Compared with traditional physical testing methods, the proposed method effectively avoids barriers to direct testing under complex operating conditions. It not only greatly reduces testing difficulty and time costs but also ensures the accuracy of load extraction and system analysis.

Vehicles doi: 10.3390/vehicles8060132

Authors: Hao Zhong Ming Zhuang Weida Wang Liuquan Yang Chao Yang Mingjun Zha Xuelong Du

With advances in electric drive technology, electric tracked vehicles (ETVs) have emerged as a promising solution for high-mobility ground vehicles. However, under high-speed steering conditions, the equivalent motor load inertia varies significantly, introducing strong nonlinear and time-varying characteristics into the ETV that may induce lateral instability and even rollover. To address this issue, a novel augmented deep Koopman operator-based model predictive control (ADK-MPC) method is proposed. First, a high-order sliding-mode (HOSM) observer is designed to estimate the lumped load disturbances associated with the time-varying equivalent motor load inertia. Then, the estimated disturbances are introduced as an augmented state into the DK operator to construct a data-driven augmented model. The proposed model transforms the nonlinear dynamics into a lifted linear time-invariant representation in the augmented-state space while capturing the dominant nonlinear characteristics. Based on the ADK model, an ADK-MPC controller is developed to convert the nonlinear optimization problem into a quadratic programming problem, thereby improving steering stability and reducing computational complexity. Simulation results under steering conditions indicate that the proposed method achieves better yaw rate tracking and lower computational cost than nonlinear MPC. The yaw rate tracking error is reduced by 45.5%, while the average solving time is shortened by 11.7%.

Vehicles doi: 10.3390/vehicles8060131

Authors: Mihaela Racila Theodor Oprica Lucian Matei Ilie Dumitru Nicoleta Gencarau Laurentiu Racila

Constrained optimization plays a central role in transport and logistics location problems, such as depot siting under geometric or infrastructure-related constraints. In practice, the classification of constrained extrema by classical second-order methods, typically based on bordered Hessians and the explicit manipulation of the total differentials of the constraint functions, can be cumbersome and error-prone, especially in engineering-oriented applications. In this paper, we present algorithmic procedures for the classification of constrained extrema in low-dimensional problems (2D and 3D), with applications to transport location models. The proposed approach does not avoid the use of constraint derivatives, since first-order constraint information is necessary for any local constrained classification procedure. Rather, it avoids the explicit manipulation of the total differentials of the constraints during the application phase. The required constraint information is incorporated through first-order partial derivatives evaluated at the stationary point, leading to simple algebraic test coefficients derived from the second derivatives of the Lagrangian. The procedures apply to regular non-degenerate cases and require only the solution of Fermat-type systems together with the evaluation of low-order determinants. Their practical relevance is illustrated through a transport depot location problem with geometric constraints, showing how the proposed approach can provide a transparent and effective decision-support tool for transport and logistics engineering.

Vehicles doi: 10.3390/vehicles8060130

Authors: Alexander Grantcharov Milka Uzunova Konstantin Dimitrov Rositsa Velichkova Iskra Simova

The work presented in this study uses Bond Graphs to model and simulate complex urban traffic flow systems consisting of three interconnected, traffic-light-controlled crossroads with heavy traffic demand. Bond Graph models are highly versatile for modeling multi-domain systems and provide a convenient bridge between analytical representations and numerical implementations. In this paper, we exploit Bond Graph model theory and digital logic concepts to develop a structured methodology for deriving Bond Graph switching network models applied to urban traffic flow. A simple traffic-light-controlled crossroad is then modeled and analyzed. Moreover, the application of Bond Graph modeling to traffic flow, illustrated through a real case study of a street network in Sofia, Bulgaria, validates the proposed model-based approach. The obtained results demonstrate the relevance and effectiveness of the proposed Bond Graph model-based macroscopic traffic modeling framework in capturing the fundamental dynamics of traffic flow under signalized control. Beyond the specific case study considered, these results highlight the potential of the approach as a general and extensible tool for modeling more complex urban traffic networks. They open perspectives for future work aimed at assessing the flexibility, scalability, and generalization capability of the framework for heterogeneous intersections and large-scale traffic systems.

Vehicles doi: 10.3390/vehicles8060129

Authors: Ghadah Alshammari Abeer Hakeem Afraa Attiah Linda Mohaisen

Most UAV missions currently require visiting multiple checkpoints to perform field tasks in environments with varying levels of obstacle complexity. These missions become more challenging because UAVs have limited onboard resources, particularly in terms of energy, making it necessary to determine a safe and efficient path that enables all required visits to be completed while minimizing both travel distance and energy consumption. To address these challenges, this study proposes a hybrid fuzzy metaheuristic approach that integrates Cuckoo Search and Tabu Search for multi-objective UAV path planning. The proposed approach generates collision-free paths in environments with static obstacles and employs fuzzy logic to construct a unified evaluation function, in which distance and energy values are mapped to membership functions and combined into a single fitness score to guide the optimization process. Cuckoo Search drives global exploration of the solution space, while Tabu Search refines solutions locally. Together, they improve path quality and avoid premature convergence. Experimental results across two scenarios with varying obstacle densities and checkpoint counts demonstrate the efficacy of the proposed hybrid approach. Compared with two baseline algorithms, the hybrid approach achieves reductions in path length ranging from 0.01% to 42.11% and in energy consumption ranging from 0.08% to 27.91%, depending on scenario complexity. Moreover, it maintains a high success rate of 96–100% as both checkpoint counts and obstacle density increase, whereas the baseline algorithms drop to 3–13% in more complex environments. These results highlight the effectiveness and scalability of the approach for multi-checkpoint UAV path planning in obstacle-rich environments.

Vehicles doi: 10.3390/vehicles8060128

Authors: Xing Liu Yaozong Bai Lijuan Liu Yunde Qin Yong Huang Ruixue Wang Xuezhong Fu

To address issues such as deformation and stress concentration that are prone to occur in pure electric passenger vehicle electric drive housings under complex working conditions, an integrated electric drive housing was taken as the research object for finite element simulation analysis and improvement tests. A finite element model of the housing was established based on ABAQUS to analyze the strain and stress of the housing and its deformation patterns under load. In response to issues such as bearing abnormal noise and seal failure caused by housing deformation, a method was proposed to enhance the structural rigidity of the housing and improve the load transfer path of the housing, which was verified through bench tests. The results showed that the maximum deformation of the improved housing decreased by 42.7%, the stress and strain in key areas were controlled within the design allowable range, and the failure rate approached zero, meeting the engineering design requirements.

Vehicles doi: 10.3390/vehicles8060127

Authors: Wenqiang Yu Shui Yu Qingmin Zhu Fangpeng Ning

Autonomous driving, as a core component of intelligent transportation systems, relies highly on precise environmental perception capabilities. Vehicle target detection is the fundamental task of environmental perception. However, complex factors in real scenarios (such as target occlusion, illumination changes, and dense traffic flow) often lead to feature misjudgments, missed detections, target positioning deviations, and category confusions in existing methods. To address these challenges, this paper proposes the MLRP-YOLOv8n model that integrates Mixed Local Channel Attention (MLCA) and large kernel separable attention (LSKA). Three complementary attention mechanisms as well as improved regression loss are integrated into the lightweight YOLOv8n architecture to improve the accuracy of vehicle detection while maintaining computational efficiency. Firstly, MLCA is embedded in the C2f feature extraction module to enhance local feature focus; the SPPF module integrates LSKA optimize multi-scale feature fusion; RFCBAMConv convolution is used to replace the original convolution in the neck to enhance cross-level feature correlation; the PIoUv2 loss function is introduced instead of Complete Intersection over Union (CIoU) to accelerate model convergence and reduce regression errors. Experiments on the KITTI Detection dataset subset and UA-DETRAC datasets show that MLRP-YOLOv8n improves the mean average precision (mAP) by 1.9% and 3.2% respectively on the KITTI Detection dataset subset and UA-DETRAC datasets. This model achieves a balance between detection accuracy, tracking robustness, and computational efficiency, providing a reliable solution for autonomous driving environment perception.

Vehicles doi: 10.3390/vehicles8060126

Authors: Hao Kong Zengxiong Peng Liuquan Yang Chao Yang Muyao Wang Ming Zhuang

Predictive energy management strategies (PEMSs) have attracted increasing attention in hybrid electric vehicles (HEVs) for improving fuel economy and powertrain efficiency using anticipated driving information. For PEMS, data-driven velocity prediction is widely used to capture complex driving patterns from historical trajectories and future traffic priors, but often lacks kinematic awareness, leading to physical causality violations and long-horizon state drift. To address these issues, this paper proposes a physics-informed PEMS, where a Physics-Informed Spatio-Temporal Network (PI-STN) provides control-oriented velocity information for an MPC-based energy management controller. Specifically, to address pseudo-motion in velocity prediction under standstill conditions, a global zero-speed gating mechanism is introduced; to suppress acceleration/deceleration trends that violate vehicle kinematic causality, a causal penalty is designed; and to mitigate temporal phase misalignment between data-driven predictions and physical motion priors, a Differentiable Kalman Filter (DKF) is incorporated. At each receding horizon step, the PI-STN-predicted velocity sequence is converted into future power demand through longitudinal vehicle dynamics and used by MPC for engine–battery power allocation under SOC and engine transient constraints. Under the same tested conditions, the proposed strategy reduces engine power fluctuation by 15.1% compared with BiLSTM-Transformer, and achieves an equivalent fuel consumption of 323.74 g, outperforming Transformer-KF by 3.12%.

Vehicles doi: 10.3390/vehicles8060125

Authors: Jiankun Xie Minkai Pan Kunhao Zhao Hao Lin Leiming Song Xiaojun Hu

As the operational service lifespan of subway rail vehicles increased, the sound insulation of the carbody inevitably deteriorated, leading to heightened noise levels inside the vehicles and significantly compromising passenger comfort. Therefore, the impact of the subway carbody’s sound insulation performance on interior noise throughout its service life was studied. The research of this paper was carried out by combining experimental and simulation methods. Through experimental testing, it examined the sound insulation levels of different vehicle components, including the door, side wall and underframe. The carbody sound insulation with different operational lifetimes was obtained. Subsequently, an acoustic simulation model for interior noise in subway vehicles was established via the statistical energy method, and measured data was used to ensure reliability. Finally, based on the simulation model, the interior noise values under different operational service lifespans were obtained. The influence patterns of varying sound insulation performance across different carbody components on interior noise levels were analyzed. The influence of the change in sound insulation over the operational lifespan on the interior noise was obtained. The findings of this paper hold practical engineering significance for developing noise control strategies and maintenance plans for subway rail vehicles.

Vehicles doi: 10.3390/vehicles8060124

Authors: Haider Khazal Ahmed Othman Alanazi Younis K. Khdir Nasser Firouzi Przemysław Podulka

This paper presents a robust dual-loop control strategy for the lateral motion and heading-angle regulation of an autonomous vehicle equipped with a Steer-By-Wire (SBW) system under unknown time-varying disturbances. The proposed framework comprises a fuzzy PID controller in the inner loop to generate the motor torque and track the front-wheel steering angle, and an optimal backstepping controller in the outer loop—integrated with a finite-time disturbance observer—to ensure lateral trajectory tracking and wind-disturbance rejection. The PID gains are tuned online by a Mamdani-type fuzzy inference system, while the backstepping parameters are optimized offline via a genetic algorithm. Beyond the bicycle-model-based design, the controller is evaluated through supplementary simulations using a 6-degree-of-freedom (6-DOF) vehicle model, as well as through a detailed robustness analysis that includes measurement noise and increasing lateral disturbance forces. The results demonstrate that the closed-loop system achieves precise path tracking, finite-time convergence of both tracking and estimation errors, and effective compensation of road vibrations and wind disturbances. Furthermore, the controller maintains stable performance under significant measurement noise and tolerates lateral disturbance forces up to at least 10,000 N without violating safety constraints. The effectiveness of the proposed method is consistently confirmed across both the reduced-order bicycle model and the higher-fidelity 6-DOF validation environment.

Vehicles doi: 10.3390/vehicles8060123

Authors: Baoquan Wei Kai Zhen Fangming Deng Jian Wang Han Zeng Yang Song Zhigang Liu

In the published publication [...]

Vehicles doi: 10.3390/vehicles8060122

Authors: Rogelio Santiago León-Japa Lainny Josue Yagloa-Tarco Anthony Joel Vinueza-Soria Juan Pablo Medina-Namicela José Luis Maldonado-Ortega

This research focuses on the application of convolutional neural networks (CNNs) for fault detection in ignition coils and fuel injectors of a YESA 3140 gasoline engine. The objective is to design a CNN capable of identifying when the spark ignition engine (SIE) is operating under optimal conditions and when it presents specific power supply disconnection faults in the four injectors and four coils. Signals from the knock sensor (KS) and camshaft position sensor (CMP) of the SIE were acquired using a MyDAQ data acquisition card and LabVIEW software version 2024. A strict sampling protocol was followed: each replicate had a duration of 5 s while the engine was running at normal operating temperature and idle speed. Prior to each sampling, the SIE was operated with the corresponding fault induced for 5 min. The signals obtained from the KS sensor were transformed into spectrograms, which were then used to train various CNN models. The resulting CNN achieved a classification error of 3.21%. The algorithm was validated by inducing supervised faults in various Otto cycle engines.

Vehicles doi: 10.3390/vehicles8060121

Authors: Guozheng Li Baijing Wu Ke Gao Guanghui Yan

To address the limited prediction accuracy caused by neglecting the inherent periodicity of spatiotemporal traffic flows during spatial feature extraction, this study develops an adaptive spatiotemporal graph convolutional method for highway traffic flow prediction. Firstly, an adaptive temporal graph generation layer with multiple time periods is constructed to dynamically generate traffic flow temporal graphs with rich representations, enabling accurate characterization of spatiotemporal traffic patterns. Secondly, a lightweight Transformer architecture is introduced to design an efficient feature extraction module, which refines both global and local spatiotemporal variations as well as their interactions. Finally, a multi-head self-attention module integrating different temporal scales is designed to capture the intrinsic correlations and dynamic dependencies across multi-scale traffic data, thereby enhancing prediction accuracy and generalization capability. Extensive experiments on two publicly available datasets, PEMSBAY and PEMSM, demonstrate the effectiveness of the proposed method. Compared with the baseline approaches, the proposed model achieves average reductions of 14% in MAE, 19% in MAPE, and 15% in RMSE. These results indicate that the proposed framework improves forecasting accuracy and provides a reliable methodological foundation for intelligent transportation systems.

Vehicles doi: 10.3390/vehicles8060120

Authors: Zacharenia Garofalaki Dimitrios Kallergis Ioannis Voyiatzis Christos Douligeris

As Intelligent Transportation Systems (ITS) transition towards automated ecosystems, the deployment of advanced wireless charging technologies becomes a critical infrastructure requirement. Central to the management of these networks is the Open Charge Point Protocol (OCPP), which ensures interoperability across diverse hardware vendors. However, the reliance on digital communication for power transfer introduces significant cybersecurity vulnerabilities. This paper presents a methodology for evaluating the impact of cyber-threats on urban transport services, with a specific focus on the communication layers that support these Advanced Wireless Power Transfer (WPT) environments. Utilising Stochastic Petri net (SPN) ontology, we model the operational states of an Electric Vehicle (EV) service—including the activation and the arrival phases—to quantify how protocol-level vulnerabilities affect service reliability. We introduce an Extended Vulnerability List (EVL) and analyse two distinct scenarios: a public transport service and a weather forecasting integration. Our results demonstrate that as wireless charging moves towards standardization, the security of the OCPP-based backbone is a fundamental necessity for preventing service disruption. The proposed assessment framework provides a roadmap for securing the next generation of dynamic wireless charging infrastructures against evolving cyber-physical threats.

Vehicles doi: 10.3390/vehicles8060119

Authors: Oleksandr Osetrov Rainer Haas

The transition to a hydrogen-based energy economy emphasizes the potential of hydrogen as a fuel for plug-in hybrid electric vehicles (PHEVs). The performance of a hydrogen engine within a PHEV depends on the choice of its operating modes, which influence both efficiency and emissions. This study proposes a method for developing engine operating lines (EOLs) on engine maps based on minimizing nitrogen oxide (NOx) emissions while considering constraints on maximum engine power. A total of 15 EOLs are proposed for configurations with both constant and variable maximum engine power. Using mathematical modeling of PHEV operation under the Worldwide Harmonized Light Vehicles Test Cycle (WLTC), the impact of EOL selection on engine characteristics, as well as on battery and generator parameters, is analyzed. For a comprehensive evaluation of EOL effectiveness, five criteria are introduced, considering fuel energy consumption, NOx emissions, wear, mechanical fatigue, and noise, vibration, and harshness (NVH). The Analytic Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) are applied to determine the weighting factors of the criteria and to rank the proposed EOLs, thereby identifying the most efficient configurations. The results show that, for the base hydrogen engine configuration, selecting appropriate operating modes alone enables NOx emissions to be reduced significantly below Euro 6 limits, without any hardware modifications or exhaust aftertreatment.

Vehicles doi: 10.3390/vehicles8060118

Authors: Songlin Xu Huiying Wen Sheng Zhao

Reducing carbon emissions from expressway systems has become increasingly important under continued growth in passenger and freight activity. Using Guangdong Province as a case study, this paper develops an evolutionary system dynamics model to compare the mitigation effects of transport structure adjustment and increasing new energy vehicle (NEV) penetration. The model integrates socioeconomic development, traffic activity, vehicle technology composition, energy use, and carbon emissions, and simulates the carbon-emission trajectory of the provincial expressway network from 2016 to 2035. The results show that expressway carbon emissions in Guangdong remain under clear upward pressure in the baseline scenario. By 2035, the NEV Growth scenario reduces emissions by 14.73% relative to the baseline, whereas the Transport Structure Adjustment scenario reduces emissions by only 2.41%. The Combined Scenario achieves the largest reduction, reaching 18.06%. These results indicate that technological substitution contributes much more to carbon mitigation than moderate structural adjustment, while the combined pathway produces the strongest overall effect. The findings suggest that expressway decarbonization policy should prioritize NEV deployment and supporting infrastructure, while treating transport structure adjustment as a supplementary pathway.

Vehicles doi: 10.3390/vehicles8060117

Authors: Liqiang Fu Huan Li Yansong Shi Zhijie Wang Chen Li Qi Huang Youshui Lu

Heavy-haul railways require efficient rail replacement because extreme axle loads and high-density transport accelerate rail wear. Traditional manual-led processes are limited by fragmented operations, high labor demand, and complex equipment scheduling, typically completing about 1 km of rail replacement within a 4 h maintenance window and requiring approximately 340 workers. This study is positioned as construction-process modeling, workflow organization, and simulation-supported feasibility analysis for an integrated rail-changing workflow, rather than the development or field validation of a fully mature rail-changing machine. The proposed workflow coordinates rail unloading, on-board welding, fastener disassembly, rail cutting, exchange-recovery, fastening, closure welding, and final inspection through a highly parallel construction organization. A process-level train-set configuration, including a tractor, a long-rail comprehensive transport vehicle, an exchange-recovery integrated transport vehicle, and a mobile welding vehicle, is used as an engineering carrier to support the closed-loop workflow of unloading, welding, exchange, and recovery. Based on engineering time-study analysis, field experience, expert consultation, and discrete-event simulation, the results indicate that the proposed workflow has the potential to complete a simulated 2 km rail-changing task within a single 4 h maintenance window with an estimated labor demand of 80–95 personnel under the specified assumptions. The study provides conceptual and simulation-supported feasibility evidence for construction-process organization, rather than field-validated machine performance, and offers a technical reference for improving the mechanization and coordination of heavy-haul railway maintenance.

Vehicles doi: 10.3390/vehicles8060116

Authors: Yordan Stoyanov

This study evaluates the feasibility, repeatability, and temporal consistency of a low-cost long-wave infrared (LWIR) thermal imaging workflow for in-vehicle driver monitoring under realistic operating conditions. Two participants were monitored during three independent 60 min driving sessions each. Facial thermal observations were obtained using a consumer-grade mobile LWIR camera operated through a smartphone application environment. Forehead-region temperature data were extracted from a manually positioned region of interest (ROI), including center-point, mean, maximum, and minimum temperature values. Geometric validation was first performed under stationary vehicle conditions in order to confirm forehead-ROI visibility and stability across multiple head orientations and posture variations. Subsequent dynamic sessions were used to evaluate cross-session repeatability and temporal behavior of sampled ROI-based thermal metrics. The results show that the facial thermal patterns remained structurally consistent across repeated sessions, while the sampled temperature trajectories exhibited generally smooth behavior without evidence of progressive within-session instability over the 60 min recordings. Although minor inter-session offsets were observed, normalized analysis confirmed preservation of the relative temporal dynamics. The findings indicate that the examined low-cost LWIR workflow can provide sufficiently stable and repeatable facial thermal observations for feasibility-level driver monitoring analysis under realistic in-vehicle conditions. The contribution of this work lies in a structured validation methodology combining geometric validation, cross-session repeatability, and temporal consistency assessment as a methodological foundation for future thermal-based driver monitoring applications.

Vehicles doi: 10.3390/vehicles8060115

Authors: Daniel Barzallo-Arce Edgar Vicente Rojas-Reinoso Daysi Baño-Morales David Calderón Herrera José Antonio Soriano

This exploratory study assessed the vehicle- and route-dependent response of five multipoint injection passenger vehicles to two commercial fuel additives marketed as octane-related gasoline additives under real-world high-altitude driving conditions in Quito, Ecuador. The tests were conducted on one urban route and one rural/peripheral route using base gasoline with a nominal octane index of RON 85, RON 85 gasoline with Additive A, and RON 85 gasoline with Additive B. Fuel economy and CO2-related indicators were obtained through the OBD-II port using the Torque Pro application; therefore, the reported values were interpreted as electronic control unit-based estimates rather than direct gravimetric fuel consumption or laboratory emissions measurements. The revised analysis used OBD-derived trip-average fuel economy as the primary response variable. The mixed-effects model showed a significant effect of route on fuel economy (p < 0.001) and a significant fuel condition × route interaction (p = 0.0089), while the main effect of fuel condition was not statistically significant (p = 0.0699). Additive B increased the mean OBD-derived trip-average fuel economy on the urban route from 11.56 to 12.60 km·L−1, but reduced it on the rural route from 13.46 to 12.65 km·L−1. At the vehicle level, the previously extreme Vehicle 3 response was revised to a more plausible increase from 11.03 to 13.64 km·L−1 (+23.68%) when trip-average fuel economy was used. Since the actual RON/MON values and physicochemical properties of the final fuel blends were not experimentally measured, the observed responses cannot be attributed exclusively to octane number enhancement. Overall, the findings indicate that commercial additive performance was vehicle- and route-dependent rather than universally beneficial. This field-based assessment supports evidence-informed decision-making for sustainable mobility and aligns with SDG 16 and SDG 17 through transparent technical evaluation and academic collaboration.

Vehicles doi: 10.3390/vehicles8050114

Authors: George Achitei Lamara Achitei Aristotel Popescu Daria Sachelarie Lidia Gaiginschi Teodor Anita Elena Adelina Chiriac

Waste heat recovery from automotive exhaust gases represents an important strategy for improving vehicle energy efficiency. This study experimentally investigates the performance of a thermoelectric generator (TEG) system based on TEC1-12706 modules running under different cold-side cooling conditions and incorporating a Hot Rolled Steel (HRS) protective layer on the hot side. The HRS plate was used to ensure uniform heat distribution and protect the thermoelectric module against thermal shocks generated by a 250 °C heat source. Four cooling regimes were experimentally analyzed: natural convection and forced airflows equivalent to 40, 60, and 90 km/h. The results proved that increasing airflow intensity significantly improved the temperature difference across the module, from approximately 16 ± 2 °C under natural convection to nearly 40 ± 2 °C at the highest airflow velocity. Correspondingly, the steady-state voltage generated increased from approximately 0.25 ± 0.01 V to over 0.60 ± 0.01 V under an 82 Ω resistive load. The measured hot-side temperature remained below 75 °C in all experimental conditions, confirming the thermal protection capability of the HRS layer. The experimental data also revealed a near-linear relationship between voltage and temperature difference, consistent with the Seebeck effect. The proposed configuration shows the feasibility of combining thermal protection and forced convection cooling to improve the stability and electrical performance of thermoelectric waste heat recovery systems intended for low-power automotive auxiliary applications.

Vehicles doi: 10.3390/vehicles8050113

Authors: Hanan Thwany Muhammad Alolaiwy Mohamed Zohdy

Electric vehicle (EV) adoption is rising rapidly, creating growing challenges for charging infrastructure planning, energy demand management, and grid stability. However, most existing studies rely on single-domain data, such as behavioral charging sessions or station metadata, which limits their ability to capture the joint effects of user behavior, charger characteristics, and market context. To address this gap, this study proposes a multi-domain machine learning framework for EV charger-type prediction by integrating behavioral, infrastructure, and market-level data. Behavioral charging logs are transformed into structured event-token sequences and modeled using XLM-RoBERTa (Cross-lingual Language Model–RoBERTa), which is used here as a transformer-based sequence encoder to capture long-range dependencies in charging behavior. Structured infrastructure and market features are modeled using LightGBM and TabNet. The study contributes a unified multi-domain framework, a systematic comparison of transformer and tabular-learning models, and a broader evaluation through ablation analysis, cross-validation, confusion matrix analysis, and confidence calibration. The results show that multi-domain fusion consistently improves performance over single-domain learning. XLM-RoBERTa achieved the best overall performance on the fused dataset, with 98.76% accuracy and 97.86% weighted F1-score, while TabNet demonstrated stronger calibration and deployment reliability.

Vehicles doi: 10.3390/vehicles8050112

Authors: Gaetano Bosurgi Alessia Ruggeri Giuseppe Sollazzo Orazio Pellegrino Domenico Passeri

Traffic calming devices (TCMs) are widely implemented to reduce urban vehicle speeds; however, their influence on drivers’ direct control inputs remains underexplored. This study examines how drivers redistribute braking, throttle and steering inputs in the presence of speed cushions, extending driver–infrastructure interaction assessment beyond speed-only metrics. A driving simulator reproduced an urban corridor in Messina (Italy). Twenty-five drivers completed three scenarios: baseline without traffic calming (No TCM), daytime with speed cushions and nighttime with speed cushions. Cushion colour (red/blue) and width (1.5, 1.8, 2.1 m) were varied. Vehicle telemetry was analyzed using repeated-measures ANOVA with corrected post hoc tests and partial η2 as effect size. The analysis was complemented by paired within-subject comparisons, bootstrap confidence intervals and additional transient indicators computed on travelled-distance windows to support transparent effect interpretation without replacing the RM-ANOVA framework. Compared with No TCM, speed cushions increased mean braking (+224% Day, +372% Night) and reduced the mean normalized throttle input by approximately 55%, with stronger braking at night. Width primarily influenced throttle release and steering corrections, whereas colour modulated braking under reduced visibility. Despite limitations related to sample size and simulation, the findings provide actionable evidence for contexts where cushion width and colour are not standardized.

Vehicles doi: 10.3390/vehicles8050111

Authors: Chun Ren Zhongxuan Xiong Kangjie Liu Jiayu Shen Dapai Shi Xuefeng Yang

With the rapid development of Formula Student competitions, higher demands are being placed on the vehicle performance of race cars. To further enhance vehicle performance, this study investigates the optimization of three key indicators: maximum speed, 0–100 km/h acceleration time, and energy consumption under the NEDC driving cycle. First, a vehicle physical model was established on the AVL CRUISE 2019 R2 platform based on the vehicle parameters, and corresponding simulation tasks were configured. Meanwhile, a numerical model was developed in MATLAB R2022a and validated by comparing the predicted maximum speed, acceleration time, and energy consumption with the CRUISE simulation results. On this basis, a genetic algorithm was employed to optimize the battery pack parallel number and the total reduction ratio so as to improve the vehicle performance. The optimized parameters were then re-imported into the CRUISE model for further simulation verification. The results indicate that, compared with the original configuration, the optimized scheme leads to a slight increase in acceleration time, while significantly improving the maximum speed and reducing the energy consumption under the NEDC cycle. Overall, the proposed optimization method effectively enhances the vehicle performance of the Formula Student electric race car.

Vehicles doi: 10.3390/vehicles8050110

Authors: Rupert Tull de Salis

Concept-phase planning of diesel-engined hybrid vehicles requires rapid engine synthesis, including brake-specific fuel consumption (BSFC) estimation, with minimal input data. Fuel savings from hybridization arise partly through engine downsizing and engine-off operation, so trade studies depend on knowing the dependence of BSFC on engine sizing and speed and load conditions. This paper presents a method for synthesizing hypothetical modern diesel engines of any given size for the purpose of trade studies. The synthesized engines match the performance and efficiency capabilities of commercially available units. Relationships are developed between rated power, rated speed, peak torque, displacement and cylinder count for four vehicle application classes. Together with a BSFC estimation method, these relationships form a complete engine synthesis chain from rated power to a full torque curve and BSFC map. Known values may be substituted, such as minimum BSFC, wherever published data are available. The method supports continuous scaling.

Vehicles doi: 10.3390/vehicles8050109

Authors: Bo Zhang Ming Xu

Multi-agent trajectory prediction serves as a critical component in autonomous driving systems, bridging environment perception, behavior understanding, and motion planning. Its outputs not only affect candidate trajectory evaluation and interactive decision-making but also directly influence downstream processes such as risk anticipation, braking and yielding, and safety margin allocation. Therefore, obtaining accurate and stable prediction results is of great importance. Although existing methods have achieved remarkable progress in single-timestep prediction accuracy, most of them still adopt an independent decoding paradigm under a sliding-window setting. As a result, during continuous online prediction, these models are prone to frequent mode switching, temporal discontinuities in overlapping segments, and local trajectory jitter, which become particularly pronounced in complex interactive scenarios such as yielding, merging, and unprotected turning. To address these issues, this paper proposes PHR-Net, a two-stage proposal-level historical retrieval framework that introduces cross-timestep historical context to perform consistency-aware refinement of current predictions on top of multimodal coarse proposals. Experiments on the Argoverse 1 benchmark show that PHR-Net achieves competitive performance under both Top-1 and Top-6 settings. PHR-Net obtains a Top-1 minFDE of 1.0834 and MR of 0.1046 and achieves an MR of 0.1027 under the Top-6 setting. In the overlapping-interval consistency evaluation, PHR-Net reduces the summed ADE to 2.08. These results show that proposal-level historical retrieval improves endpoint reliability and cross-timestep temporal consistency.

Vehicles doi: 10.3390/vehicles8050108

Authors: Alexander G. Mirnig Sandra Trösterer Mark Colley

When driving a partially automated vehicle, maintaining situation awareness is essential for users to be better prepared to take over. A primary challenge is maintaining awareness while the user is occupied with another task without tunneling attention towards individual elements. To investigate this, we conducted an experimental study in our driving simulator (n = 20) comparing an indirect LED (light-emitting diode) visualization of relevant objects in the driver’s field of view with a combined condition of an indirect LED + direct HUD (head-up display) visualization. The participants’ situation awareness scores were higher under the combined condition. However, the scores dropped significantly for objects outside the LED + HUD visualization. We conclude that the indirect object indication is not effective in countering tunneling effects from the HUD, and neither does it provide a satisfactory trade-off when deployed on its own, i.e., without direct indication in addition.

Vehicles doi: 10.3390/vehicles8050107

Authors: Alisher Baqoyev Azizjon Yusupov Sakijan Khudayberganov Bauyrzhan Sarsembekov Utkir Khusenov Aleksandr Svetashev Shokhrukh Kayumov Muslima Akhmedova Mafratkhon Tokhtakhodjayeva

The main objective of this study was to reduce the dwell time of wagons at stations and to improve the efficiency of shunting locomotive utilization. This is a combinatorial problem, since an increase in the number of loading and unloading fronts leads to a sharp growth in the number of feasible service variants. During the research, a mathematical model describing the servicing process of industrial sidings was developed. This study addressed the problem of determining the optimal sequence of wagon deliveries and the optimal distribution of workload among shunting locomotives. For conditions under which two or more shunting locomotives are used, an optimization method based on the indicator of wagon-hour reduction (σ) was proposed for allocating loading and unloading fronts. Using combinatorial properties, it was shown that many possible allocation variants are symmetric, which allowed for the development of a mathematical solution that simplifies the search for an optimal solution. Computational results demonstrated that, at the hypothetical railway station “N-1”, applying the optimal service sequence reduces wagon dwell time by 21% compared with an arbitrary sequence. At the hypothetical station “N-2”, distributing wagon groups between two shunting locomotives improves the efficiency of the servicing process by 26% compared with using a single locomotive. The results based on real data from the “B-2” railway station show that the proposed method provides an improvement of approximately 31.3% compared to the current operational practice, while Smith’s rule achieves an improvement of 14.9%. Based on the proposed model and algorithm, a software tool was developed to automatically determine servicing sequences for loading and unloading fronts, analyze alternatives, and evaluate shunting locomotive efficiency.

Vehicles doi: 10.3390/vehicles8050106

Authors: Biming Zhao Yiman Dong Shulei Sun Kunfan Liu Xiaorong Huang Bojiang Chen Wenyan Zhang

In mixed pedestrian–vehicle traffic environments, overtaking pedestrians by vehicles is a prevalent and complex human–vehicle interaction scenario. However, this maneuver often leads to accidents, resulting in injuries and fatalities, primarily due to inadequate in frastructure, limited pedestrian safety awareness, and suboptimal driver behavior. To mitigate such accidents and develop active vehicle safety systems and autonomous driving algorithms based on human–vehicle interaction data, it is crucial to investigate the overtaking behavior of human drivers. This study examines driver overtaking behavior under various conditions through driving simulator experiments and evaluates how different experimental variables influence driver performance. Using data from 12 skilled drivers, a risk corridor for vehicles overtaking pedestrians is established and a lateral distance prediction model is developed. Based on this established risk corridor, a vehicle overtaking strategy is proposed. Furthermore, to assess the risk level associated with overtaking pedestrians, pedestrians’ subjective risk perceptions are quantified. The simulation results indicate that the maximum lateral error of the vehicle is approximately 0.14 m, the maximum heading error is about 0.06 radians, and the vehicle’s trajectory during pedestrian overtaking remains within the defined risk corridor. These findings are consistent with the operational characteristics of human drivers.

Vehicles doi: 10.3390/vehicles8050105

Authors: Yong Chen Yuhai Li Yuguo Xu Baitan Ma Qing Zhou

To address the stochasticity of real-world driving conditions and the optimality of energy allocation in a hybrid electric vehicle (HEV), this paper proposes a model predictive control (MPC) energy management strategy based on the Stacked–CNN–BiLSTM–Attention (SCBA) network. First, an SCBA-based vehicle speed prediction model is constructed by enhancing the bidirectional long short-term memory (BiLSTM) network with a double-layer convolutional structure and an attention mechanism, enabling the model to extract and fuse temporal features of the speed sequence, thereby overcoming the insufficient characterization of local abrupt speed variations and improving the accuracy of speed prediction. Secondly, a novel global optimization algorithm, the Rüppell’s Fox Optimizer (RFO), which possesses strong global search capability, is embedded as the solver for the multi-objective optimization problem in a rolling-horizon MPC framework, delivering superior energy-saving performance. Simulation results show that, compared with the conventional BiLSTM model, the proposed speed prediction model reduces the maximum root-mean-square error (RMSE) by 46.12% and the end-point prediction RMSE by 62.6%. The proposed RFO-MPC energy management strategy reaches 97.04% of the fuel-saving performance of dynamic programming (DP), representing a 5.6% improvement over the DP-MPC strategy. Finally, the effectiveness of the energy management strategy (EMS) is verified by hardware-in-the-loop (HIL) testing.

Vehicles doi: 10.3390/vehicles8050104

Authors: Songpo Yang Yumiao Wu Mengjiao Zhao

The crew re-scheduling problem (CRSP) is a critical challenge in the operation and management of urban rail transit (URT) systems, especially when restoring service after disruptions. When a crew member unexpectedly leaves duty due to emergency events like illness, the train assigned to that crew may get stranded in one operating direction, which will block the following trains operating in the same direction. To address this issue, this study first introduces a closed-loop driving strategy. This strategy reallocates limited crew resources across both operating directions to maintain the basic operations of the URT system during emergency periods. On this basis, an integer linear programming (ILP) model is developed to describe the dynamic adjustments of train departure times. Valid inequalities are incorporated to generate feasible crew task sets rapidly, and the proposed model is solved by using Gurobi. To meet the stringent time requirements for rescheduling during disruptions, an improved greedy algorithm is further designed to manage crew assignment under emergency conditions efficiently. Finally, the effectiveness of the proposed approach is evaluated through a real-world case study based on the Beijing urban rail transit network. The results demonstrate that the proposed model can respond rapidly within 30 min after an incident occurs. It not only limits the generation time of each crew task to within 1 min but also achieves a relative working balance between crews by combining short-duration tasks.

Vehicles doi: 10.3390/vehicles8050103

Authors: Sebastian Wagner Dieter Schramm

Designing strongly interacting vehicle components in the early development phase is challenging because numerous requirements, uncertainties, and conflicting objectives significantly limit feasible design solutions. Achieving optimal commonality is particularly complex when a single component must satisfy the requirements of multiple systems. Solution space engineering is an effective method for identifying robust common solutions and has been successfully applied to components with linear properties. However, its applicability is limited for components with non-linear properties, as their properties vary with the operating point. Consequently, evaluating component commonality across systems cannot rely solely on functional properties, since these are operating-point-dependent and system-specific. Both boundary conditions and quantities of interest differ between systems and must be considered to avoid unnecessary restriction of the solution space during development. This paper presents an extension of solution space engineering for developing common components with non-linear properties, explicitly accounting for differing system requirements at identical operating points. An enhanced layering technique is introduced that establishes commonality at the level of component design variables. The proposed approach is demonstrated through the design of rear axle subframe mounts.

Vehicles doi: 10.3390/vehicles8050102

Authors: Mohammed Chadli

The landscape of automotive and transportation engineering is evolving at an unprecedented pace, driven by a growing demand for safer and smarter mobility [...]

Vehicles doi: 10.3390/vehicles8050101

Authors: Taotao Hu Xiufeng Zhao Luxia Yang

To address the challenges of insufficient feature fusion and incomplete multi-scale information capture in complex traffic scenarios, we propose a vehicle type recognition network based on feature comparison and the Mixture of Experts (MoE) model. Specifically, the MobileNetV4 backbone is introduced to enhance deep feature extraction for vehicle targets. Meanwhile, we design a Multi-scale Interleaving Fusion Module (MSIFM), which progressively transmits feature channels via an interleaving structure to capture multi-scale features while enhancing vehicle feature representation. Moreover, we devise a Feature Compare Enhancement Module (FCEM) to efficiently fuse feature maps with different semantic information. By performing feature comparison, it strengthens strongly correlated features while suppressing weakly correlated ones. Finally, we design a Mixture of Experts Feature Enhancement Module (MOEFEM) to aggregate multi-scale feature maps and adaptively capture detailed vehicle features through multiple expert units. Experimental results demonstrate that our method achieves mAP improvements of 2.2% and 2.4% over YOLOv11 on UA-DETRAC and BDD100K, respectively. The proposed method not only improves detection accuracy significantly but also maintains real-time efficiency, providing a practical solution for high-precision vehicle type recognition. It offers valuable technical support for intelligent transportation systems, smart city management, and autonomous driving safety.

Vehicles doi: 10.3390/vehicles8050100

Authors: Joseph Barreiro-Zambrano Juan Martinez-Parrales Roberto López-Chila

Inadequate vehicle maintenance management is one of the main causes of road accidents and elevated operating costs in light vehicles. This paper addresses this problem through the development and implementation of a low-cost integrated system for preventive maintenance management and alerts. The device, based on an open-hardware architecture (Arduino Mega 2560), integrates Global Positioning System (GPS) and mobile communication (GSM/LTE) modules to monitor distance traveled in real time and notify the user via SMS about the proximity of critical services such as oil changes, brake inspections, and timing-belt replacements. Its technical contribution lies in the integration of non-intrusive virtual ignition, filtered GPS-based odometry, configurable MicroSD-based persistence, and progressive SMS alert logic into a low-cost aftermarket system for conventional vehicles without OBD-II dependence. Experimental validation was conducted in the city of Guayaquil using a 2012 Hyundai Accent. Field tests were carried out in three scenarios: a dense urban route, a peripheral road, and interurban routes. Results showed satisfactory accuracy with a global average percentage error of 3.98% compared to the vehicle’s odometer and 100% effectiveness in sending alerts under the tested conditions (20/20 events; exact 95% binomial confidence interval: 83.2–100.0%). These results provide strong evidence of technical feasibility for the proposed architecture under the tested conditions in a representative single-vehicle proof-of-concept, while broader cross-vehicle validation remains necessary before generalizing the system to the wider diversity of aging fleets.

Vehicles doi: 10.3390/vehicles8050099

Authors: Juan Diego Valladolid Juan P. Ortiz

Accurate energy consumption prediction is fundamental for enhancing range estimation and trip planning in battery electric vehicles (BEVs) under real-world conditions. This study develops a route-level benchmark utilizing 1 Hz data acquired via ECU/OBD-II interfaces (CAN 500 kbps) across ten diverse real-world driving routes. The input feature set comprises vehicle speed, longitudinal acceleration, estimated motor torque, road altitude, and accelerator pedal position. Ground truth energy consumption was derived from battery voltage and current, integrated via the trapezoidal rule. We performed a comparative analysis between five memoryless regressors (FNN, SVR, GPR, QRNN, and Bagged Trees) and three sequence models (LSTM, GRU, and BiLSTM) trained on 20-second temporal windows. The results indicate that the GRU model achieved the highest overall performance (mean RMSE = 0.1142 kWh, R2 = 0.9545 and MAE = 0.072 kWh), while Bagged Trees emerged as the most robust static model (mean RMSE = 0.1587 kWh). Temporal models outperformed static ones on routes with high dynamic variability, whereas Bagged Trees excelled in five specific scenarios. These findings provide a controlled within-route benchmark for time-resolved cumulative energy estimation and highlight the need for chronological and cross-route validation before drawing deployment-oriented generalization claims.

Vehicles doi: 10.3390/vehicles8050098

Authors: Jin Jiang Jidong Dai Wei Li Yuquan Zhou Maozhang Ye Jianhuan Zhang Chentao Zhang

This paper presents UM3D, an end-to-end unsupervised domain adaptation framework for monocular 3D object detection. Monocular 3D object detection is appealing due to its low cost, yet it suffers from limited depth cues and poor cross-domain generalization when labeled data are scarce. Existing Pseudo-LiDAR methods require supervised training and propagate depth estimation errors to downstream detection, while current unsupervised domain adaptation (UDA) approaches exploit only a single modality and lack effective pseudo-label quality control. UM3D addresses these limitations through two key designs: (1) a quality-aware pseudo-label generation strategy with object-level random scaling and a memory bank refinement mechanism; and (2) an end-to-end differentiable pipeline that integrates multimodal fusion of image and Pseudo-LiDAR features with a multi-network consistency loss, which jointly optimizes depth estimation and 3D detection via backpropagation. Notably, the entire pipeline requires only a single monocular camera at inference; the Pseudo-LiDAR representation is generated internally from the same image, and thus the multimodal fusion integrates image and Pseudo-LiDAR features without requiring additional sensors. Extensive experiments across KITTI, nuScenes, Waymo, and Lyft demonstrate that UM3D generally outperforms existing UDA methods. In particular, a 19.30% relative APBEV improvement is achieved under easy conditions through end-to-end joint training compared to independent depth estimation, and up to 76.81% of the domain gap is closed on the WOD → KITTI benchmark.

Vehicles doi: 10.3390/vehicles8050097

Authors: Hamza El Yanboiy Mohammed Chaman Mohammed Bouabdellaoui Adam Khechchab Youssef El Merabet

This study investigates the safety limitations of autonomous vehicles operating under dense fog conditions, where sensor performance is severely degraded, and explores the potential of cooperative control for collision avoidance. A comparative framework is developed using the CARLA simulator to analyze four driving configurations: no perception and no communication, degraded LiDAR–radar sensing, V2V-assisted Model Predictive Control (MPC), and V2V-assisted MPC enhanced with predictive buffering. The methodology integrates fog-dependent perception modeling, cooperative hazard messaging, and real-time MPC-based longitudinal control, and evaluates system performance through multiple simulation trials under urban and highway conditions. Key performance indicators include time-to-collision, reaction time, maximum deceleration, jerk, and collision occurrence. The results demonstrate that perception-only strategies lead to late reactions and unsafe emergency braking, with minimum TTC values as low as 0.29 s and frequent collision events. In contrast, V2V-assisted MPC significantly improves anticipation and driving comfort, while the proposed predictive buffering approach achieves a 0% collision rate and increases the minimum TTC to approximately 1.93 s. The inclusion of predictive buffering further enhances robustness against communication losses, enabling smoother deceleration and consistently safe inter-vehicle spacing. Overall, the findings confirm that cooperative V2V communication combined with predictive control effectively compensates for fog-induced perception degradation and represents a viable solution for improving safety and reliability in low-visibility autonomous driving scenarios.

Vehicles doi: 10.3390/vehicles8050096

Authors: Hongbin Tang Zehui Huang Jingchun Wang Jianjiao Deng Shibin Wang Zhiguo Zhang Zhenjiang Wu

Automotive plastic parts present multiple challenges for Computer-Aided Engineering (CAE) simulation modeling, including complex thin-walled geometries, difficulties in meshing fine features (e.g., clips and snap-fits), and time-consuming manual processing with inconsistent quality. To address these issues, this paper proposes an automated method for generating mid-surface meshes. The proposed approach integrates AI-based feature recognition, point cloud registration, and geometric fitting. First, a specialized point cloud dataset consisting of 132,000 samples of plastic part features was constructed. Using a PointNet++ model, precise semantic segmentation of typical features, such as clips and backing plates, was achieved. Subsequently, a library of typical features was established, and an FPFH-ICP point cloud registration strategy was implemented. Based on the matching rate, an adaptive selection between two processing paths, direct standard mesh replacement and segmentation-fitting generation was performed. For features with low matching rates, a suite of segmentation-fitting algorithms was proposed. These algorithms incorporate incomplete cylinder parameter extraction, Monte Carlo boundary identification, and internal point cloud reordering, thereby facilitating high-quality mid-surface mesh generation for complex topological structures. Finally, experimental validation was conducted on typical automotive interior plastic parts as well as on new cross-platform vehicle models. The results demonstrate that the proposed method reduces mesh modeling time by 67% while preserving the accuracy of geometric feature restoration. The mesh quality compliance rate increases from 52.27% to 90.9% with the proposed method, reaching a level comparable to that of professional manual meshing. In cross-platform validation, the proposed method maintained high accuracy. Consequently, this approach significantly enhances the intelligence and engineering reliability of CAE pre-processing, providing effective technical support for the automated simulation modeling of complex thin-walled components.

Vehicles doi: 10.3390/vehicles8050095

Authors: Liqian Wang Yantao Liang Dehai Zhang Xufan Wang Tong Xing Yang Song

To support the safe operation and technological promotion of existing line speed-up projects, this paper presents an assessment method for pantograph–catenary contact performance under the 200 km/h speed conditions, using the Guangzhou–Shenzhen Lines I and II speed-up projects as representative case studies. Based on the ANCF method, a refined pantograph–catenary coupling dynamic model is established to accurately characterize the large deformation and geometric nonlinear behavior of the catenary system. Model validation is achieved using actual measurement data from the CR400AF train. Based on this model, systematic simulation analyses were conducted to evaluate the current collection performance of four mainstream train models—CR300AF, CR400BF, CRH380A, and CRH380B—under both single-unit and double-unit operation conditions. Results indicate that dynamic contact force metrics for pantograph–catenary interactions meet all limit requirements specified in the Technical Specifications for Dynamic Acceptance of High-Speed Railway Projects under all operating conditions. This demonstrates that the pantograph–catenary system on the analyzed Guangzhou–Shenzhen Line exhibits excellent dynamic stability and safety under the targeted speed-up scheme, providing simulation-based justification for implementing the speed enhancement project.

Vehicles doi: 10.3390/vehicles8040094

Authors: Wenpeng Sun Yu Zhang Nengchao Lyu

To meet the high-fidelity testing environment requirements for autonomous driving system development, driving simulators are gradually evolving from tools that “only provide scenes and interaction interfaces” into integrated verification platforms for autonomous driving capabilities. These simulators, in particular, need to feature testable and scalable decision-making modules. However, the autonomous driving functions in existing driving simulators mostly rely on rule-based or simplified model approaches, which are inadequate for depicting the complex interactions in real-world traffic and fail to meet the personalized decision-making needs under various driving styles. To address these challenges, this paper designs and implements a trajectory data-driven personalized autonomous driving decision system, using drone aerial imagery as the core data source to provide realistic background traffic flow and human-like decision-making capabilities. The proposed system can be interpreted as an integrated decision–planning–control framework deployed within a high-fidelity driving simulation platform. It consists of a driving style classification module based on drone trajectory data, a personalized decision module integrating inverse reinforcement learning and dynamic game theory, and a planning and control module. First, a natural driving database is built using 4997 real vehicle trajectories, and prior features of different driving styles are extracted through trajectory feature engineering and an improved K-means++ method. Based on this, a personalized decision-making framework that combines dynamic game theory and maximum entropy inverse reinforcement learning is proposed, aiming to learn the preference weights of different driving styles in terms of safety, comfort, and efficiency. Furthermore, the Dueling Network Architecture (DuDQN) is used to generate human-like lane-changing strategies. Subsequently, a real-time closed-loop execution of personalized decisions in the simulation platform is achieved through fifth-order polynomial trajectory planning, lateral Linear Quadratic Regulator (LQR) control, and longitudinal cascade Proportional–Integral–Derivative (PID) control. Experimental results show that the personalized decision model trained with drone data can realistically reproduce vehicle decision-making behaviors in natural traffic flows within the simulation environment and generate autonomous driving strategies that are highly consistent with different driving styles. This significantly enhances the humanization and personalization capabilities of the autonomous driving module in the driving simulator.