Vehicles, Volume 8, Issue 8 (August 2026) – 29 articles

This paper presents an adaptive event-triggered nonsingular fast terminal sliding-mode control (AETSMC) framework for quadrotor unmanned aerial vehicles subject to aerodynamic disturbances, actuator loss of effectiveness (LOE) of up to 60%, and intermittent denial-of-service (DoS) attacks. First, a nonsingular fast terminal sliding-mode (NFTSM) surface is constructed using fractional powers of the tracking error rather than fractional-order derivatives. This design ensures finite-time convergence while avoiding the singularity associated with conventional terminal sliding-mode schemes. Second, a smooth positive-semidefinite barrier function (Smooth-PSBF) is incorporated into the adaptive gain law. The resulting law provides only the compensation required to maintain the prescribed bound, thereby limiting gain overestimation and chattering. Third, a dual-mode event-triggering mechanism combines an exponentially decaying threshold with a zero-order hold. A positive lower bound on the inter-event interval is derived from the closed-loop dynamics, which excludes Zeno behaviour. Simulations under matched conditions show that the proposed method reduces the pitch-channel root-mean-square error by 79.4% and the integral squared error by 95.8% relative to the first reproduced baseline. In a separate 15-s communication experiment sampled at 1 kHz, the controller generated 128 transmissions instead of 15,000 periodic updates, corresponding to a 99.15% reduction. These results indicate that the proposed framework can improve fault-tolerant tracking while reducing communication demand under intermittent DoS attacks. Full article

The three-dimensional documentation of crash-damaged vehicles can support the visual and geometric recording of deformation, but it is unclear how complete mobile reconstruction workflows compare when applied to the same vehicles. This study compared three workflows using a single consumer device, an Apple iPhone 16 Pro Max: reconstruction from photographs, reconstruction from extracted video frames, and direct mobile light detection and ranging (LiDAR) scanning. Three damaged vehicles were documented: a Volkswagen Passat B6 Variant, a Toyota Auris, and a Toyota Yaris. RealityScan was used for reconstruction from photographs and video frames, and Polycam was used for the LiDAR scans. In CloudCompare, all models were cleaned, scaled using the known wheelbase, registered to the LiDAR reference by the Iterative Closest Point algorithm, and compared using cloud-to-mesh distances, with the principal quantitative statistics based on absolute point-to-surface distance magnitudes. Because the mobile LiDAR model served as an internal reference rather than as an independent metrological ground truth, the reported values describe residual post-registration point-to-surface deviations and not absolute geometric accuracy. The principal surface evaluation used exactly 100,000 surface-sampled points per evaluated direction and bidirectional cloud-to-mesh calculations. The standardised results did not show a uniform ordering between reconstruction from photographs and reconstruction from video frames. In the reconstruction-to-LiDAR direction, median absolute distances ranged from 0.03082 to 0.03677 m for the Passat, from 0.02900 to 0.03413 m for the Auris, and from 0.05953 to 0.06168 m for the Yaris. Lower reverse-direction median values and the broader upper-tail distributions observed for the Yaris demonstrated the directional character of the surface comparison. The Yaris showed larger, long-tailed deviations concentrated mainly in the rear and left-lateral damaged regions. However, because each damage configuration was represented by only one vehicle, the observed differences cannot be attributed to damage type alone. The three workflows provided complementary geometric and visual information for crash-damaged vehicle documentation, although model fusion and accident-reconstruction parameters were not evaluated in this study. Because only one acquisition was performed for each vehicle–workflow combination, the findings should be interpreted as an exploratory comparison rather than as an assessment of repeatability, operator variability, or measurement uncertainty. Full article

Urban transport emissions are a major contributor to climate change and urban air pollution. Although previous studies have demonstrated that traffic demand, fleet electrification, and driving behavior individually influence vehicular emissions, their combined effects under different congestion conditions remain insufficiently understood. This study investigates the interactions among these factors using the microscopic traffic simulator SUMO (Simulation of Urban MObility). A synthetic urban corridor consisting of five signalized intersections was developed to represent arterial roads in medium-sized cities. A full factorial experimental design was implemented by considering three traffic demand levels, three electric vehicle adoption percentage levels, and three driving behavior profiles, resulting in 27 experimental scenarios with 10 stochastic replications per scenario. Traffic performance and pollutant emissions were evaluated to quantify both the individual and interaction effects of the experimental factors. The results indicate that traffic demand is the primary determinant of CO₂ and NO_x emissions, while fleet electrification substantially reduces emissions, particularly under congested conditions. Driving behavior also plays a role by influencing acceleration and deceleration patterns. Furthermore, statistically significant interaction effects among the experimental factors ($p<0.05$) reveal the benefits of fleet electrification considering the traffic demand and the driving behavior. These findings contribute to the understanding of sustainable urban mobility by providing a comprehensive assessment of how traffic demand, fleet electrification, and driving behavior jointly influence urban traffic performance and vehicle emissions, offering valuable insights for the design of integrated transportation and environmental policies. Full article

Ride quality—encompassing vehicle comfort, vibration isolation, and noise, vibration, and harshness (NVH)—has become a key competitive differentiator in modern automobiles. This paper presents a comprehensive update to the foundational literature on passenger car ride quality, capturing the rapidly growing literature and new technological paradigms that have emerged over the past decade. Established approaches to human vibration response, vehicle dynamics modelling, and road surface characterisation are examined within the ISO 2631 framework. This review critically surveys advances driven by battery electric vehicle (BEV) powertrains—where the absence of internal combustion engine noise unmasks motor whine, inverter switching noise, and tyre–road excitation, lowering the perceptual ride–NVH boundary from ~25 Hz toward 15–18 Hz—as well as intelligent semi-active and active suspension technologies, deep reinforcement learning for suspension control, machine learning for ride quality prediction, and connected vehicle infrastructure enabling predictive preview control. Key research gaps are identified: the absence of validated ISO 2631 frequency weightings for autonomous vehicle postures, the lack of standardised open benchmark datasets for cross-study comparison, and the unresolved sim-to-real validation gap for data-driven suspension controllers. Ten priority research directions are proposed for the coming decade. Full article

Lane-changing decision-making is a critical component of autonomous driving, as it requires balancing safety, efficiency, and manoeuvre stability under dynamic traffic conditions. This study proposes a hybrid Deep Deterministic Policy Gradient (DDPG) framework with an MPC-inspired predictive safety layer for autonomous lane-changing in a controlled highway overtaking scenario. The DDPG policy generates candidate longitudinal commands and lateral lane-change intentions, while the supervisory layer evaluates the predicted evolution of the target-lane front gap, rear gap, and time-to-collision (TTC) over a short prediction horizon before permitting the lateral manoeuvre. Rather than solving an online MPC optimisation problem, the supervisory layer employs short-horizon state prediction and constraint-based safety assessment to determine whether the candidate lane-change intention meets the predefined safety and overtaking-necessity conditions. The proposed framework is evaluated in a unified Simulation of Urban MObility (SUMO) highway environment and compared with rule-based, MPC-only, and DDPG-only controllers using consistent scenario conditions and performance metrics. The evaluation considers task success, collision occurrence, overtaking time, average speed, safety-related spacing, driving comfort, and lane-change behaviour. The results show that all evaluated controllers completed the overtaking task without collisions under the considered scenario. However, the proposed hybrid controller achieved the shortest mean overtaking time, the highest mean speed, the largest minimum front-gap margin, and a single lane change per episode. These findings indicate that combining learning-based decision-making with lightweight short-horizon predictive safety supervision can improve overtaking efficiency and lane-change consistency while maintaining safe vehicle interactions. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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 ($r_{\mathrm{rb}}=-0.759$ 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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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 (PSV_m10; IGS $=1.89$ vs. IGS $=2.30$ for the WLTC). Ten vehicles (Euro III–V) were instrumented with OBD-II and portable analysers recording $\mathrm{CO}$, $\mathrm{NO}$, $\mathrm{HC}$, and ${\mathrm{CO}}_2$ at 1 Hz over a four-year campaign (2021–2025; ≈2000 h). K-Means clustering on PSV_m10 identified five operating regimes (silhouette ≈0.384). Under dynamically equivalent aedc conditions, $\mathrm{NO}$, $\mathrm{CO}$, and $\mathrm{HC}$ all peaked in the 1000–2000 m band ($\mathrm{NO}$: $0.188\mathrm{g}{\mathrm{km}}^{-1}$, $6.7\times$ the sea-level value; $\mathrm{CO}$: $4.47\mathrm{g}{\mathrm{km}}^{-1}$, $+50\%$; $\mathrm{HC}$: $0.047\mathrm{g}{\mathrm{km}}^{-1}$, $+292\%$), fell in the 2000–3000 m band, and partially rebounded above 3000 m ($\mathrm{NO}$: $0.186\mathrm{g}{\mathrm{km}}^{-1}$); ${\mathrm{CO}}_2$ instead declined monotonically with altitude (182 to $119\mathrm{g}{\mathrm{km}}^{-1}$, $-35\%$), tracking a near-stable-to-slightly-declining fuel consumption (8.56 to $8.11\mathrm{L}/100\mathrm{km}$) 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. Full article

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. Full article

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. Full article

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 $i_q$, 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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article