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.

Vehicles doi: 10.3390/vehicles8040093

Authors: Hong-Tao Tang Fa-Rui Zhao Zi-Hao Zhang Yu-Liang Liu Xiu-Ming Cao

Existing research primarily focuses on ordinary straight roads or curves; however, there is a notable lack of recent research on continuous curves. This research employs Computational Fluid Dynamics (CFD) dynamic mesh technology to numerically simulate the external flow field during vehicle overtaking on a continuous curve resembling a sine wave. This study conducts a numerical simulation to analyze the external flow field of vehicles during overtaking on a continuous curve, similar to a sine curve, using CFD. Using different initial velocities, the study analyzes lateral force on the vehicle body during overtaking. It investigates how dynamic changes in the external flow field affect vehicle dynamics by employing tetrahedral meshes, the SST k-ω turbulence model, and UDF programming. To address emergency overtaking scenarios during medical vehicle rescues, a four-factor orthogonal experimental design was employed to identify the safest overtaking condition: overtaking a small vehicle (5 m × 1.8 m) at 22 m per second with 1.5 times the vehicle width and no crosswind. Regression lines were fitted to the data, yielding a nonlinear regression equation that can predict road conditions, thereby providing theoretical support for intelligent driving systems.

Vehicles doi: 10.3390/vehicles8040092

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

This paper presents an iterative graph-analytical procedure for determining the roll center height, one of the most critical design parameters influencing vehicle dynamic behavior during cornering. The conventional approaches generally determine roll center locations from suspension kinematics and then evaluate vehicle behavior using multibody or numerical vehicle dynamics models. By contrast, the proposed method is intended for the preliminary design stage and provides a direct correlation between front and rear target roll center heights using tire test data, load transfer and axle-level equilibrium conditions. The main advantage of the method is that it helps define a feasible design space before detailed geometry optimization or MBD validation is performed. The objective is to achieve stable and neutral handling (avoiding intrinsic understeer or oversteer tendencies) during steady-state cornering at a predefined target lateral acceleration. The methodology integrates (i) lateral force equilibrium at the axle level, (ii) a dynamic load transfer model based on axle roll stiffness and roll center heights, and (iii) experimental tire grip characteristics (lateral force–slip angle curves under varying vertical loads), processed through numerical interpolation. The procedure is demonstrated using a vehicle model with specific geometric and mass parameters. The results indicate that the methodology does not yield a single unique solution, but rather a set of correlated roll center heights, allowing the designer to select the most feasible geometric configuration while maintaining neutral handling. As an example, the paper presents a convergent solution for the front and rear roll center heights that satisfy neutrality conditions at a slip angle of approximately 4°. This study provides a fundamental framework for the geometric design of suspension systems and serves as a basis for subsequent numerical and experimental validation.

Vehicles doi: 10.3390/vehicles8040091

Authors: David Abellán-López Francisco J. Simón-Portillo Abel R. Navarro-Arcas Miguel Sánchez-Lozano

The electrification of urban and industrial transport is driving the need for propulsion architectures that combine energy efficiency, operational flexibility and regulatory compliance. However, current electric platforms often lack the adaptability required for customized body configurations and multistage manufacturing, and their approval is hindered by the complexity of meeting electrical safety and electromagnetic compatibility (EMC) requirements at vehicle level. This article presents the conceptual design of a modular electric propulsion module developed within the MODULe project, in which the traction motor, inverter, battery pack, Battery Management System (BMS) and cooling circuits are integrated into a standardized module conceived as an Independent Technical Unit (ITU). The propulsion module dimensioned using a modified WLTP cycle, and the results indicate that the selected components can meet the dynamic demands of light and medium-duty vehicles, achieving an estimated consumption of around 50 kWh/100 km and a driving range above 160 km. By concentrating the critical regulatory requirements within a single module, the proposed architecture facilitates multistage vehicle approval, reduces development effort and supports the scalable electrification of commercial fleets. This approach may contribute to accelerating the deployment of zero-emission vehicles in urban logistics and industrial applications, with potential benefits for both the sector and society.

Vehicles doi: 10.3390/vehicles8040089

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

In automotive Computer-Aided Engineering (CAE), the fidelity of high-quality shell element meshes is fundamentally governed by the accuracy of mid-surface geometry extraction. Conventional manual extraction for complex automotive plastic components is labor-intensive, error-prone, and often compromises mesh quality. To address these issues, this paper proposes an automated mid-surface mesh reconstruction method based on point cloud registration, establishing an integrated framework comprising “Multimodal Registration—Displacement Binding—Surface Correction.” Using a source part with an ideal mid-surface as a template, the method integrates Random Sample Consensus (RANSAC) and Iterative Closest Point (ICP) for rigid registration and Coherent Point Drift (CPD) for non-rigid registration to achieve high-precision alignment between the target and source outer-surface point clouds. Subsequently, a K-Nearest Neighbor (K-NN) search-based displacement binding mechanism smoothly transfers the outer-surface displacement field to the source mid-surface point cloud. Following position correction and surface smoothing, a complete and high-quality target mid-surface mesh is generated. Experimental results on typical plastic snap-fit components demonstrate that the normal projection error between the generated mid-surface and the manually refined “gold standard” mesh is less than 0.05 mm. The processing time per component is approximately 38 s, representing an efficiency improvement of over 73% compared to manual extraction using commercial CAE software. This method effectively mitigates common issues such as mid-surface distortion and feature loss, offering a high-precision, fully automated solution for automotive CAE pre-processing.

Vehicles doi: 10.3390/vehicles8040090

Authors: Julio Saucedo-Soto Viridiana Hernández-Herrera Moisés Márquez-Olivera Octavio Sánchez-García Antonio-Gustavo Juárez-Gracia

Given that approximately 70% of traffic accidents are attributable to driver-related factors, it is necessary for vehicles to incorporate technologies that reduce risk through preventive actions derived from traffic-scene analysis. Interpreting the driving environment is non-trivial and is commonly decomposed into sub-tasks; among them, traffic light perception is critical due to its role in regulating vehicular flow. This paper evaluates five YOLO CNN families (YOLOv8–YOLOv12) on two tasks: (i) traffic light detection and (ii) traffic light state recognition (green, yellow, red). The evaluation uses a hybrid dataset comprising the public LISA traffic light dataset and a custom dataset with images from Mexico City captured under diverse lighting conditions—a relevant setting given the city’s high traffic intensity. The results show mAP@0.50 = 94.4–96.3% for traffic light detection and mAP@0.50 = 99.3–99.4% for traffic light state recognition, indicating that modern YOLO variants provide highly reliable performance for both tasks under natural illumination variability.

Vehicles doi: 10.3390/vehicles8040087

Authors: Vincenzo Palermo Marco Gabiccini Eugeniu Grabovic Massimo Guiggiani Matteo Pergoli Luca Bergianti

This paper presents a simulation-based methodology for automated tuning of a triple-loop controller (steering, throttle, and braking) for a Dallara single-seater race car. The approach targets on-track driving at handling limits, where strong nonlinearities and coupled dynamics dominate, treating the vehicle as a black box. Five controller gains are optimized via derivative-free pattern search, using reference trajectories from a professional driver in a Driver-in-the-Loop (DiL) simulator. Human-likeness is promoted by penalty terms on state and control trajectories while maximizing distance over a fixed horizon as a proxy for lap-time reduction. The application uses a high-fidelity multibody vehicle model with realistic tire, suspension, and actuator dynamics in the DiL environment, rather than simplified single-track representations. Contributions are: (i) effective application of derivative-free optimization to complex, high-dimensional, black-box vehicle systems; and (ii) a systematic, reproducible procedure for automatic tuning of controller parameters with a predetermined architecture to reproduce a professional driver’s performance and embed human-likeness. Optimization required approximately 2.4 h. Results show that the optimized controller improves track coverage by 63.6 m (1.1% increase) compared to manual tuning while maintaining a realistic driving style, offering a more systematic and reliable solution than manual, trial-and-error calibration.

Vehicles doi: 10.3390/vehicles8040088

Authors: Yavar Taghipour Azar Juan Diego Ortega Marcos Nieto

As automated vehicles move from controlled environments to unpredictable real-world roads, scenario-based testing has become the cornerstone of safety validation. In recent years, substantial progress has been made in scenario representation standards and generation methodologies. However, integrating scenario generation, standards-aligned packaging, validation, curation, and structured querying into a reproducible end-to-end lifecycle remains challenging in practice. This work presents a reproducible reference architecture for Scenario Databases (SCDBs) that treats scenario collections as lifecycle-governed data systems rather than static repositories. The proposed architecture unifies the scenario lifecycle within a single workflow. It integrates scenario generation and ingestion, validation and curation, immutable storage, semantic and value-based querying, and reproducible export. Scenario semantics are represented using ASAM OpenX formats (OpenDRIVE and OpenSCENARIO), together with ASAM OpenLABEL metadata, enabling standards-aligned interoperability. Querying is performed over categorical and value-carrying metadata without requiring inspection of raw scenario artifacts at query time. The reference implementation is deployed using Infrastructure-as-Code, supporting reproducibility and low operational overhead. Execution-based metric enrichment is supported as an optional extension, enabling scenarios to be augmented with execution-derived measurements and trace metadata. The contribution is not a centralized database, but a reference architecture and deployment blueprint that supports interoperable and federated scenario ecosystems. By framing SCDBs as reproducible lifecycle systems, this work supports scalable scenario reuse and more transparent safety validation workflows.

Vehicles doi: 10.3390/vehicles8040086

Authors: Qiusheng Liu Zhiliang Song Xiaoyu Xu Jian Wang Joan P. Lazaro

To address the emergency obstacle-avoidance problem of intelligent vehicles on structured roads, this paper proposes an integrated planning and control method that combines an improved Artificial Potential Field (APF) with fuzzy Model Predictive Control (MPC). Different from a direct APF + MPC combination, the planning layer introduces a braking-distance threshold, an effective obstacle-influence boundary, and sinusoidal shape factors to reshape the obstacle repulsive field and alleviate local-minimum behavior. A seventh-order polynomial smoothing strategy is then adopted to generate a reference path with higher-order continuity. For trajectory tracking, a fuzzy adaptive MPC controller adjusts the prediction horizon and control horizon online according to lateral error, while a fuzzy PID controller regulates longitudinal speed. MATLAB/Simulink and CarSim co-simulation results in single-static, double-static, and double-dynamic obstacle scenarios show that the proposed method can generate smoother trajectories and achieve more stable tracking, thereby improving obstacle-avoidance safety and ride comfort. In the double-static scenario, the peak lateral error is reduced from about 0.7 m to within 0.1 m, while in the double-dynamic scenario the longitudinal speed is maintained within 78–80 km/h instead of dropping to about 67 km/h under the baseline controller. The study provides a practical technical framework for integrated decision-planning-control design in structured-road intelligent vehicles.

Vehicles doi: 10.3390/vehicles8040085

Authors: Zakaria Benali Amina Hamoud

Unmanned Aerial Systems (UAS) offer sustainable solutions for urban last-mile logistics, yet existing navigation algorithms struggle with the complexity of dynamic metropolitan environments. This study optimises a reinforcement learning (RL)-based guidance, navigation, and control (GNC) algorithm using a Proximal Policy Optimisation (PPO) model within a high-fidelity simulation of Bristol City Centre. The primary contribution is training the RL model to autonomously detect and avoid dynamic obstacles, specifically manned aircraft, to ensure safe and legal drone operations. Additionally, flight operations are continuously monitored via a Structured Query Language (SQL) database to verify compliance with low airspace regulations. Simulation results demonstrate that the proposed framework achieves high obstacle detection accuracy under nominal conditions, while the implementation of curriculum learning significantly enhances the system’s adaptability and recovery capabilities during high-speed, dynamic encounters.

Vehicles doi: 10.3390/vehicles8040084

Authors: Weiwei Lv Ke Chen Yuhan Liu Ligetu Bi Mingming Dong

Classical soil mechanics models are inadequate for predicting the traction of wheeled vehicles under high-velocity off-road conditions due to the complex dynamic soil response. To address this, this study proposes a velocity-segmented dynamic compression-shear model for aeolian sandy soil, enhancing classical theories with velocity-dependent corrections for the 0–10 m/s range. A theoretical patterned wheel–soil interaction model is developed, incorporating lug effects via an equivalent radius. Furthermore, a comprehensive vehicle traction model is established by integrating the soil model with a dynamic equilibrium iteration method that couples suspension dynamics, pitch attitude, and axle load distribution. Validation results demonstrate that the single-wheel traction theoretical model achieves an error of less than 18%, while the full vehicle traction model reaches a 73% prediction accuracy for drawbar pull and sinkage, as verified through soil bin tests and full-vehicle experiments. This research provides theoretical framework for the real-time and accurate prediction of wheeled-vehicle traction performance on unprepared terrain, offering significant improvements for high-velocity off-road mobility analysis.

Vehicles doi: 10.3390/vehicles8040083

Authors: Binbin Qin Bolin Zhang Jiangbo Qian

In recent years, the number of deaths caused by traffic accidents has continued to rise. According to investigations, approximately one-fifth of accidents are caused by drivers being distracted. With the rapid development of convolutional neural networks (CNNs) in the field of computer vision, many researchers have developed CNN-based network models to recognize distracted driving actions. However, many models have too many parameters, making them unsuitable for deployment in actual vehicles. To address this issue, we propose a multiscale driver distraction detection network called ICK-PANet, which combines attention, lightweight incremental convolution kernels, and lightweight pyramid convolution to quickly and accurately identify driver distraction actions. First, ICK-PANet uses lightweight incremental convolution kernels to capture global information and driving action details effectively. Then, it introduces lightweight pyramid convolution and attention modules to extract multistage features, thereby expanding the network’s receptive field to improve the recognition ability of key features. Finally, it fuses multistage features to predict the results. ICK-PANet was experimentally evaluated on two public datasets: the American University in Cairo Distracted Driver (AUC) dataset and the StateFarms dataset (SFD) provided by the Kaggle competition platform. The AUC and SFD accuracies are 95.66% and 99.84%, respectively, which are higher than those achieved by many other state-of-the-art methods. ICK-PANet requires only 0.4M parameters, making it one of the most lightweight models currently available.

Vehicles doi: 10.3390/vehicles8040082

Authors: Zhuang Li Zhenqi Fang Yao Fang Shaoxuan Luo

This paper presents a distributed hierarchical model predictive control (MPC) framework designed to ensure dynamic consensus and stability in nonlinear vehicle platoons, addressing challenges posed by mixed communication topologies and hard constraints. By directed graph modeling of the mixed communication topologies, the dynamic consensus goal for the platoon is defined by the inter-vehicle distances between the host and its neighbors, whereas the stability criterion for an individual vehicle is expressed as a positive definite function of its position and velocity deviations. Then, a contractive constraint is elegantly designed to correlate these two objectives in a hierarchical model predictive control framework, where the lower layer optimizes the stability objective and the upper layer optimizes the dynamic consensus objective. The conditions ensuring stability and string stability for the vehicle platoon are shown to be only dependent on the deviations of the host vehicle, which achieves dynamic consensus and string stability simultaneously for nonlinear vehicle platoons. Several representative scenarios are used to validated the performance of the proposed strategy.

Vehicles doi: 10.3390/vehicles8040081

Authors: Nagendra Kumar Anubhav Agrawal Rajeev Kumar Manoj Badoni

Accurate estimation of battery state of health (SoH) is an important aspect for improving the reliability, safety, and operating efficiency of an energy storage system. This study presents a unified deep learning pipeline for prediction, latent feature extraction, and anomaly detection. A convolution neutral network autoencoder is used to learn compact latent features from a dataset (NASA battery datasets, i.e., B0005, B0006, B0007, and B0018). These features serve as inputs to random forest and linear regression models, which are further compared with the CNN and GRU. The system is evaluated using leave-one-group-out cross-validation to ensure robustness across different batteries. Latent space quality is studied using PSA, t-SNE, and UMAP analyses. Furthermore, clustering performance is measured using the Silhouette Score, and anomalies are detected using reconstruction error and the Isolation Forest technique. The obtained results show that the AE+RF model achieves the best performance, with a 0.0285 root mean square value (RMSE) and a 0.0109 mean absolute error (MAE), with a high 0.96 coefficient of determination (R2). It is evident that AE+RF shows high prediction accuracy and model reliability. The results show that latent features improve prediction accuracy, helping to clearly separate normal and abnormal patterns, providing a robust and accurate approach to battery SoH estimation that is suitable for battery management system applications.

Vehicles doi: 10.3390/vehicles8040080

Authors: Weiwei Song Yuning Liu Xinke Zhao Yi Zhang Xinye Dai Shimin Zhang

Train track occupancy detection is essential for railway operation safety and dispatching, yet GNSS-based positioning and track matching can degrade or fail in turnouts and station yards due to multipath, interference, and dense track layouts. This paper presents an IMU-only method to discriminate track-switching events during turnout passage by exploiting the transient change in heading angular velocity. The Z-axis gyroscope measurement (approximately aligned with the track-plane normal) is used as a heading-rate proxy, and a lightweight indicator is constructed from the difference between a short-window moving average and the full-run mean. The full-run mean further serves as an in situ approximation of the gyroscope zero bias, alleviating the need for pre-calibration and improving robustness to systematic drift. A fixed discrimination threshold is determined from stationary gyroscope noise statistics, and the minimum effective operating speed is derived by combining gyro noise characteristics with the kinematic relationship among train speed, turnout curvature radius, and heading rate. Field experiments conducted from January to April 2025 on three railway sections covering 27 turnouts (300 turnout-passage events) show that, using a constant threshold T0=0.002rad/s, the proposed method achieves 100% track-switching discrimination accuracy within 5–40 km/h, without requiring track maps, GNSS, or prior databases.

Vehicles doi: 10.3390/vehicles8040079

Authors: Yi Liu Changxin Li Meng Jiang

To address unstable localization and sparse mapping for autonomous vehicles operating in GPS-denied and low-visibility environments, this paper proposes GTS-SLAM, a tightly coupled dense visual SLAM framework integrating Generalized Iterative Closest Point (GICP) and 3D Gaussian Splatting (3DGS). The system is designed for intelligent driving platforms such as underground mining vehicles, inspection robots, and tunnel autonomous navigation systems. The front-end performs covariance-aware point-cloud registration using GICP to achieve robust pose estimation under low texture, dust interference, and dynamic disturbances. The back-end employs probabilistic dense mapping based on 3DGS, combined with scale regularization, scale alignment, and keyframe factor-graph optimization, enabling synchronized optimization of localization and mapping. A Compact-3DGS compression strategy further reduces memory usage while maintaining real-time performance. Experiments on public datasets and real underground-like scenarios demonstrate centimeter-level trajectory accuracy, high-quality dense reconstruction, and real-time rendering. The system provides reliable perception capability for vehicle autonomous navigation, obstacle avoidance, and path planning in confined and weak-light environments. Overall, the proposed framework offers a deployable solution for autonomous driving and mobile robots requiring accurate localization and dense environmental understanding in challenging conditions.

Vehicles doi: 10.3390/vehicles8040078

Authors: Gaël Atheupe Gordan Kongue Meli Valentin Carvalho Anton Van Wyk

Contemporary vehicle development, particularly for overactuated platforms, demands design methodologies that bridge the gap between high-level performance targets and hardware selection. Existing physics-based models, while essential, offer limited utility for this systems-level design task. This paper introduces a novel analytical framework for vehicle lateral dynamics, predicated on a reformulated single-track model that integrates the concept of tire-specific slip. The derived specific slip-based bicycle model enables a comprehensive frequency-domain analysis of handling characteristics, articulated through three fundamental metrics: the front and rear axle specific slips and the vehicle wheelbase. Our results quantify the influence of these parameters on key handling attributes, including stability, responsiveness, and roll susceptibility. This work provides a constitutive tool for the model-based design of next-generation vehicles, enabling the a priori selection and optimization of chassis hardware to meet predefined performance objectives and informing the synthesis of advanced motion control systems.

Vehicles doi: 10.3390/vehicles8040077

Authors: Oana-Victoria Stanciuc-Otat Burkhard Scholz Ilie Dumitru Cosmin Berceanu

Within the continuous development of automotive safety and increasingly stringent crash regulations under the Vision Zero initiative, physical crash testing remains essential for assessing occupant injury risk. This study focuses on the evaluation of occupant dynamics in full-overlap frontal collisions, based on real crash tests. Key parameters influencing injury severity, including impact speed, seat belt usages, and occupant anthropometry, were analyzed to identify worst-case scenarios. Frontal crash test protocols from regulatory and consumer programs were included in the analysis. Physical tests were conducted according to FMVSS 208 using Hybrid III 50th percentile male and 5th percentile female dummies. Both belt-restrained and unrestrained (unbelted) conditions were considered. Numerical simulations using LS-DYNA are used as a complementary tool to support and extend the interpretation of the experimental findings, particularly in assessing the influence of impact speed, seat belt usage, and occupant anthropometry on injury metrics. The results evaluate the factors with the greatest impact on injury risk and demonstrate the importance of physical frontal crash tests in the evaluation of the occupant protection. All experimental tests were carried out at IAV Vehicle Safety.

Vehicles doi: 10.3390/vehicles8040076

Authors: Talha Ahmed Pan Lu Ying Huang

Adverse weather conditions significantly degrade mobility and safety at rural signalized intersections, where high approach speeds and limited driver expectancy amplify operational and crash risks. While autonomous vehicles (AVs) have the potential to improve traffic performance, it takes a significant duration to penetrate. During this period, mixed traffic with human drivers and AVs will dominate. In this mixed traffic, the impacts of AVs at low penetration levels on adverse weather remain insufficiently understood, particularly in rural contexts. This study presents a simulation-based assessment of the effects of low AV penetration on mobility and safety at a rural signalized intersection under varying weather conditions. A calibrated microsimulation model was developed using PTV VISSIM to represent clear, rain, and snow scenarios with autonomous vehicles introduced at low penetration rates within conventional traffic. Mobility performance was evaluated using delay, travel time, and average speed, while safety impacts were assessed through surrogate safety measures extracted using the Surrogate Safety Assessment Model (SSAM), including time-to-collision and post-encroachment time. Results indicate that low levels of AV penetration of 10% can improve overall mobility performance compared with conventional traffic, particularly under adverse weather conditions. Safety outcomes show a reduction in conflict frequency and severity under low AV penetration, with more pronounced benefits observed during degraded weather scenarios. Further AV penetration from 10% to 25% may not significantly improve in a rural environment. The findings suggest that early-stage AV deployment may offer measurable mobility and safety benefits at rural signalized intersections, even before widespread adoption. This study provides practical insights for transportation agencies and policymakers regarding the potential role of low-penetration AV integration in enhancing rural traffic operations and safety under adverse weather conditions.

Vehicles doi: 10.3390/vehicles8040075

Authors: Tao You Hao Ding Zhongwei Ni Youshui Lu

This study conducts an in-depth investigation into the left–right lifting height deviation in the main lifting and lining device of the SQS-300K tunnel and bridge clearance cleaning vehicle under specific working conditions. Through field measurements and theoretical analysis, the research highlights the typical characteristics of this issue in transition curves (with a maximum deviation of 50 mm) and its adverse effects on track geometry. A systematic hydraulic–electrical synergistic optimization scheme using independent cylinder control is proposed to address the problem. Field tests show that the maximum deviation is reduced to below 10 mm after optimization. The findings not only resolve the technical challenges encountered in the field application of the SQS-300K machine but also provide a theoretical foundation and practical technical support for the optimized design, precise control, and condition maintenance of lifting and lining devices in similar large-scale railway maintenance machinery. This contribution is significant for ensuring railway operational safety.

Vehicles doi: 10.3390/vehicles8040072

Authors: Cristian Percembli Lucian Miron Mohanad Aldhaidhawi Radu Chiriac

Liquefied petroleum gas (LPG) is a widely available alternative fuel, easily stored in liquid form, capable of displacing diesel fuel in compression-ignition engines. Bio-LPG extends this pathway because it is a renewable drop-in form of LPG; its distinguishing advantage is not a different in-cylinder combustion chemistry, but a lower life-cycle greenhouse-gas intensity that depends on feedstock and production route. This review, therefore, combines a systematic synthesis of CI-engine LPG combustion evidence with a Bio-LPG transition perspective. A PRISMA-guided search of major databases (2000–2025) yielded 47 studies with matched diesel baseline. Evidence was categorized by LPG utilization pathway, distinguishing between fumigation, gaseous port injection, and in-cylinder LPG direct injection (gaseous or liquid), alongside engine class, pilot fuel fraction, and key operating parameters (injection timing/quantity, intake conditioning, exhaust gas recirculation (EGR), and boost). Data were normalized as percentage deviations relative to diesel and synthesized across standardized load bins (25/50/75/100%). Among studies reporting nitrogen oxides (NOx), 20 of 37 showed net reductions, while results in 12 studies were load-dependent; particulate matter (PM), smoke, and soot indicators decreased in 17 of 27 cases. While intake-path strategies generally reduced NOx and smoke, they often increased CO and HC emissions at low loads. The limited emerging liquid-phase direct-injection evidence shows the closest diesel-like efficiency response, although the evidence base remains limited. Overall, the engine-level findings identify the most promising LPG/Bio-LPG deployment pathways, while the specific additional climate benefit of Bio-LPG lies in its lower well-to-wheel greenhouse-gas intensity.

Vehicles doi: 10.3390/vehicles8040074

Authors: Binbin Qin Bolin Zhang

Cognitive or visual distraction caused by drivers using mobile phones, operating the central console, or conversing with passengers while driving is a significant contributing factor to road traffic accidents. Aiming to solve the problem that existing driving behavior monitoring systems exhibit insufficient recognition accuracy and low real-time detection performance in complex driving environments, this study proposes a dual-flow driver distraction detection model based on Sobel edge detection (DFSED-Model). The model is designed with a collaborative learning framework: the first flow adopts a lightweight pre-trained backbone network to achieve efficient semantic feature extraction. The second flow utilizes Sobel edge detection to extract the driver’s driving contours and enhances the model’s spatial sensitivity to driving movements and hand movements. Through the feature learning process of the first-flow-guided auxiliary branch, collaborative optimization of knowledge transfer and attention focusing is realized, thereby improving the model’s convergence speed and discriminative performance. The proposed model is evaluated on three widely used public datasets: the State Farm Distracted Driver Detection (SFD) dataset, the 100-Driver dataset, and the American University in Cairo Distracted Driver Dataset (AUCDD-V1). Under the premise of maintaining low computational overhead, the accuracy of the DFSED-Model reaches 99.87%, 99.86%, and 95.71%, respectively, which is significantly superior to that of many mainstream models. The results demonstrate that the proposed method achieves a favorable balance between accuracy, parameter count, and efficiency, and possesses strong practical value and deployment potential.

Vehicles doi: 10.3390/vehicles8040073

Authors: Yangyang Luo Junjie Li Ruibao Jin Shenghui Xu

Unmanned aerial vehicle (UAV) technology is becoming increasingly integrated into the full lifecycle management of expressways, emerging as a vital tool in the intelligent transformation of transportation infrastructure. However, existing research is fragmented, lacking systematic integration and in-depth exploration of common challenges. This paper uses a systematic literature review (SLR) to examine UAV application scenarios, technological advancements and implementation outcomes in expressway planning, design, construction, operation and maintenance. The findings reveal that UAVs have achieved critical applications in all phases, including topographic surveying, progress monitoring, identifying defects, and monitoring the structural health of infrastructure. This has significantly enhanced management efficiency. However, its large-scale deployment along long-distance linear infrastructure in open traffic environments faces systemic barriers, including inefficient data acquisition and processing, hardware limitations in endurance and payload, insufficient algorithm generalization under sparse distress patterns and complex backgrounds, operational uncertainties caused by meteorological and electromagnetic interference, and regulatory constraints related to airspace control and data compliance. Based on these findings, the paper proposes five future research directions: enhancing autonomous perception in complex environments; establishing lightweight, real-time processing frameworks; deeply integrating digital twin platforms; advancing swarm coordination technologies; and developing standardised regulatory systems. This study systematically integrates knowledge in this field, identifies current technical bottlenecks and provides a clear evolutionary path for subsequent research and applications. The study has significant theoretical value and provides practical guidance for advancing the digital and intelligent transformation of highway infrastructure.

Vehicles doi: 10.3390/vehicles8040071

Authors: Marek Nad Matus Danko Dusan Koniar Michal Frivaldsky

Regular inspection of ignition systems in internal combustion engine (ICE) vehicles is essential as these checks influence both engine performance and emission levels. While emission testing is mandatory for road vehicles, many industrial combustion devices remain outside routine emission control. During standard service procedures such as oil changes, the ignition system can be evaluated using electronic diagnostic tools, which are commonly available in licensed service stations. These measurements provide valuable insight into the spark plug condition—a critical factor affecting ignition quality and emission formation. This article presents the design of a diagnostic system based on an oscilloscope equipped with voltage and current probes. Experimental data were obtained directly from test vehicles and include waveform records of electrical quantities, revealing clearly distinguishable differences in component behavior. The proposed system enables rapid and accurate spark plug condition assessment under various operating states. Results confirm that the selected diagnostic approach can identify characteristic variations in ignition components, thereby improving fault detection accuracy. This study introduces an innovative, non-intrusive diagnostic method applicable to the development of modern automotive tools. Overall, this work contributes to enhancing the reliability, efficiency, and emission performance of internal combustion engines.

Vehicles doi: 10.3390/vehicles8040070

Authors: Shih-lin Lin Wenbin Wan

The development of intelligent connected vehicles (ICVs) continues to be shaped by simultaneous advances in sensing, communication, autonomy, and data-driven system design [...]

Vehicles doi: 10.3390/vehicles8040069

Authors: Iulius Alexandru Tudor Florin Gîrbacia

Over the past decade, road safety research has experienced rapid development due to the rapid expansion of large crash databases, the adoption of artificial intelligence techniques, and the demand for proactive and predictive safety solutions. This study conducts a data-driven review of recent research trends in transport safety. It focuses on main domains including crash severity analysis, human factors, vulnerable road users (VRUs), spatial modeling, and artificial intelligence applications. A systematic search of the Scopus database identified 15,599 relevant scientific papers published between 2016 and 2025. After constructing this corpus, titles, abstracts, and keywords were preprocessed using a natural language pipeline. The analysis employed BERTopic, a transformer-based topic modeling framework. The analysis identified 29 distinct research topics, further synthesized into five major thematic areas: (1) crash severity and injury analysis, (2) driver behavior and human factors, (3) vulnerable road users, (4) artificial intelligence, machine learning, and computer vision in intelligent transportation systems, and (5) spatial analysis and hotspot detection. A notable increase in publications related to artificial intelligence and machine learning has been evident since 2020. The results show a transition from descriptive, post-crash studies to integrated, multimodal, predictive analysis. Overall, the findings reveal a paradigm shift in the field. This study also identifies ethical and economic issues associated with the use of artificial intelligence in intelligent transportation systems, including data management, infrastructure requirements, system security, and model transparency. The results signify a transition from intuition-based models to explainable, spatially explicit, and data-intensive models, ultimately facilitating proactive risk assessment and informed decision-making.

Vehicles doi: 10.3390/vehicles8030068

Authors: Md Tufajjal Hossain Joyoung Lee Dejan Besenski Lazar Spasovic

On heavily traveled highway corridors, traffic congestion, lane merges, toll facilities, and complex interchanges frequently trigger sudden and aggressive deceleration, commonly referred to as harsh braking (HB). Such maneuvers reflect near-miss driving conditions that may precede crashes. Traditional traffic safety analyses rely primarily on historical crash records, a reactive approach that limits agencies’ ability to identify and address emerging risks in a timely manner. Because HB events are continuously captured by connected-vehicle telematics, they provide an opportunity to evaluate roadway safety risk more proactively. This study investigates the applicability of harsh braking events as a surrogate indicator of crash risk on New Jersey interstate highways. The analysis uses more than 8.5 million connected-vehicle telemetry records from Drivewyze and approximately 45,000 police-reported crashes collected between July and December 2024. HB events were identified using a deceleration threshold of 6 ft/s2 (approximately 0.2 g) and spatially matched to one-mile highway segments along with crash data. Spatial analysis shows that both HB events and crashes are highly concentrated along major corridors, including I-95, I-80, I-78, and I-287, with notable clustering near toll plazas and complex interchange areas. Temporal patterns indicate that harsh braking activity increases substantially during late fall, likely reflecting seasonal congestion and adverse weather conditions. To quantify the relationship between HB events and crash frequency, Negative Binomial (NB) and Zero-Inflated Negative Binomial (ZINB) regression models were estimated at the segment level. Results reveal a positive and statistically significant association between HB events and crash counts. In the preferred ZINB model, each additional HB event is associated with approximately a one percent increase in expected crash frequency. While the effect of individual events is small, repeated harsh braking activity corresponds to a meaningful increase in crash risk; for example, an increase of 10 HB events corresponds to an expected crash frequency of about 10% higher. Overall, the findings suggest that connected-vehicle HB data can complement traditional crash records by providing early indications of elevated risk. Incorporating HB monitoring into highway safety programs may support proactive identification of hazardous locations and more timely deployment of targeted countermeasures.

Vehicles doi: 10.3390/vehicles8030067

Authors: Karzan Ismael

Public bus systems are vital to achieving sustainable urban mobility in developing countries; yet, the quality of bus stops, a critical interface between users and transit services, remains widely overlooked. This study evaluates bus stop quality in Sulaymaniyah, Iraq, from bus users’ perspectives by integrating importance–performance analysis (IPA) and the customer satisfaction index (CSI) with level of conformity analysis (CR) using extensive, real-world survey data. The objective was to identify priority areas to help improve the quality of public bus stop provision in the city and ensure the most efficient allocation of resources by focusing on the quality attributes that matter most to bus users. The results highlight six critical service quality attributes that require immediate improvement due to their high importance to users and low service quality performance: (i) safety barriers to prevent traffic accidents while waiting at bus stops; (ii) accessibility of bus stops for elderly and disabled users; (iii) availability of signage and timetables/maps; (iv) overall bus stop quality; (v) narrow bus stop platforms; and (vi) waiting time at bus stops. Addressing these gaps is essential to enhance user satisfaction and ensure that users have a safer, more inclusive, and reliable PT experience. This study offers evidence-based recommendations to enhance bus stop design and service quality, thus contributing to improved user satisfaction and increased ridership. More broadly, the results can be applied to other rapidly urbanizing developing cities seeking to provide equitable, safe, and user-centered bus transit systems.

Vehicles doi: 10.3390/vehicles8030066

Authors: Elena-Daniela Lupu Ștefan Lucian Tabacu

Environmental regulations, rapid technological advancements, and evolving mobility trends have led to a significant transformation of the automotive industry in recent years. The adoption of battery-electric vehicles (BEVs) has been accelerated by these developments, which are becoming increasingly efficient and widely deployed. Evaluating BEV energy consumption and performance is essential for optimizing energy efficiency, extending driving range, and developing effective control strategies under real-world operating conditions. The analysis is based on the WLTP Class 3 driving cycle, in which the vehicle operating points are projected onto the motor efficiency map to evaluate the influence of real-world operating conditions on overall propulsion efficiency. Two operating scenarios are considered: with regenerative braking and without regenerative braking. The inverter and battery are modeled using quasi-static energy-based representations to ensure system-level energetic consistency while maintaining computational efficiency. The results show that rotor topology significantly influences vehicle-level energy consumption. The dual-layer IPM configuration reduces net WLTP energy demand by approximately 9% and increases the estimated driving range from about 489 km to 535 km compared to the single-layer V-shaped configuration. Variations in rotor topology led to different efficiency distributions, which leads to systematic differences in battery energy demand and achievable driving range. The results highlight the importance of aligning traction motor design with realistic operating-point distributions rather than optimizing solely for peak efficiency or marginal improvements in regenerative braking performance.

Vehicles doi: 10.3390/vehicles8030065

Authors: Hongyang Liu Zhifei Chen

To satisfy the requirements of voltage support and fault-current limitation for electric-vehicle (EV) charging stations connected to weak distribution networks, an increasing number of charging infrastructures are adopting grid-forming (GFM) converters and vehicle-to-grid (V2G) control strategies. Under AC short-circuit faults and voltage-sag conditions, limiting the fault current injected by the converter is essential to reduce overcurrent risk to power semiconductor devices. For this, an adaptive virtual-impedance-based low-voltage ride-through (LVRT) method is proposed for GFM converters in EV charging stations. First, an overcurrent grading criterion is constructed based on real-time current measurements. In the moderate-overcurrent region, an adaptive virtual reactance is introduced to achieve soft current limiting. In the severe-overcurrent region, hard current limiting is implemented by further increasing the virtual reactance and blocking overcurrent bridge arm. In addition, a virtual-reactance recovery mechanism is designed to ensure smooth post-fault restoration. Based on an RCP + HIL platform, experiments are conducted to validate the proposed fault current-limiting method. Experiment results demonstrate that the proposed method can rapidly suppress fault current, maintain voltage-support capability, and shorten post-fault restoration time.

Vehicles doi: 10.3390/vehicles8030064

Authors: Sara Atef

Accurate short-term traffic delay prediction is essential for effective intersection management and real-time traffic control. Although deep learning models have shown strong predictive capabilities in traffic forecasting, the influence of input feature configuration on prediction performance remains insufficiently understood. This study investigates how different feature groups affect short-term delay prediction at an urban roundabout using high-resolution, approach-level traffic data collected at one-minute intervals. Five feature scenarios are evaluated, ranging from temporal indicators only (S0) to a comprehensive feature set combining historical delay, operational traffic indicators, demand measurements, and temporal context (S4). Two recurrent neural network architectures, Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM), are examined under two forecasting horizons (1-min and 5-min ahead). To ensure robustness, each configuration is trained through repeated runs and evaluated using statistical significance analysis. Results show that the temporal-only baseline produces the largest prediction errors (MAE ≈ 22.5 s), while scenarios incorporating operational traffic indicators significantly improve prediction accuracy. The full feature configuration (S4) achieves the best performance for the 1-min horizon, reaching MAE values of 17.24 s and 17.22 s for GRU and LSTM, respectively. For the 5-min horizon, prediction errors increase and performance differences between feature scenarios become smaller. Additional experiments across multiple approaches confirm the general consistency of the proposed framework, while hyperparameter sensitivity analysis indicates limited dependence on model capacity. Overall, the findings highlight the importance of operational traffic indicators—particularly queue dynamics and stop patterns—for reliable short-term delay forecasting and provide practical guidance for designing efficient real-time traffic prediction systems.

Vehicles doi: 10.3390/vehicles8030063

Authors: Xin Geng Jixing Wang Shengying Guo Jiapeng Wang

In wireless power transfer (WPT) systems, magnetically coupled wireless power transfer has become a major research focus due to its advantages such as long transmission distance, strong tolerance to misalignment, and high power transfer capability. It is also widely applied in vehicle wireless power transfer systems. From the perspective of practical engineering applications, this paper investigates the problem of system parameter variations caused by changes in inductance and load, in combination with magnetically coupled structures. During actual system operation, misalignment of the coupling mechanism leads to variations in mutual inductance, while the load resistance may also fluctuate. These parameter changes result in alterations to the overall output characteristics of the system, which are detrimental to stable system operation. Moreover, adopting a dual-side communication control strategy is susceptible to interference from the system’s power circuitry. To address these issues, this paper proposes a novel state variable modeling method and designs a state observer based on the extended Kalman filter (EKF) algorithm to estimate the secondary-side parameters, thereby enabling observation of the voltage across the load at the receiver side. The state observer is configured with two operating modes to monitor variations in mutual inductance and load resistance. The observer outputs are compared with the actual load-side voltage, and the effectiveness of the proposed state observer is verified.

Vehicles doi: 10.3390/vehicles8030062

Authors: Xiaoya Gao Jiaxin Li Xujie Feng

Peak-hour congestion in metro systems poses significant challenges to operational reliability and passenger experience. This study investigates a coordinated operational strategy that integrates passenger flow control, reservation-based entry, and skip-stop train operations to alleviate congestion in high-density metro corridors. A mathematical optimization model is formulated to jointly capture passenger demand, station crowding, and train capacity constraints, and is solved using an adaptive large neighborhood search algorithm. Numerical experiments based on a real-world metro line demonstrate that the proposed framework can effectively reduce passenger waiting time and improve the balance of passenger distribution across stations under peak-hour conditions. The results indicate that coordinating multiple operational measures yields better performance than applying individual strategies in isolation, highlighting the practical value of the proposed approach for congested metro systems.

Vehicles doi: 10.3390/vehicles8030061

Authors: Yordan Stoyanov Atanasi Tashev Penko Mitev

This study evaluates the feasibility of using two affordable thermal cameras (UNI-T UTi260M and UTi260T), which are not designed as automotive sensors, for observing pedestrians and warm objects during night-time driving under low-illumination conditions. The experimental setup includes mounting the camera on the vehicle body (e.g., side mirror area/roof), recording road scenes in urban and rural environments, and selecting representative frames for qualitative and quantitative analysis. The study assesses: (i) observable pedestrian detectability in unlit road sections and under oncoming headlight glare, where visible cameras often lose contrast; (ii) the influence of low ambient temperature and strong cold wind on image appearance (including “whitening”/contrast shifts); and (iii) workflow differences, where UTi260M relies on a smartphone application for streaming/recording, while UTi260T supports PC-based image analysis and temperature-profile visualization. In addition, a calibration-based geometric method is proposed for approximate pedestrian distance estimation from single frames using silhouette pixel height and a regression model based on 1/hpx, valid for a specific mounting configuration and a known subject height. Results indicate that both cameras can highlight warm objects relative to the background and support visual pedestrian identification at low illumination, including in the presence of oncoming headlights, with UTi260M showing more stable behavior in parts of the tests. This work is a feasibility study and does not claim Advanced Driver Assist Systems (ADAS) functionality; it outlines limitations, repeatability considerations, and a minimal set of metrics and procedures for future extension. All quantitative indicators derived from exported frames are explicitly treated as image-level proxy metrics, not as physical sensor characteristics.

Vehicles doi: 10.3390/vehicles8030060

Authors: Maksymilian Mądziel Paulina Kulasa Tiziana Campisi

Plug-in hybrid electric vehicles (PHEVs) are expected to reduce fleet CO2 emissions, but real-world operation often differs markedly from type-approval values. Using European OBFCM data for 457,555 PHEVs (2021–2023) from 14 manufacturers, we quantify the “test-to-reality” CO2 gap and assess whether traction battery capacity contains an independent signal or mainly reflects vehicle segmentation and in-use behavior. Battery capacity shows only limited standalone explanatory power, while controlling for segment, monitoring year, and manufacturer and incorporating OBFCM-derived usage indicators greatly improves model fit and substantially reduces the apparent battery–gap relationship. We further find strong heterogeneity across vehicle segments, indicating that battery size is not a universal lever of real-world PHEV CO2 performance. Overall, the results support interpreting battery capacity primarily as a proxy for market positioning and real-world usage (notably charging/engine-dominant operation), highlighting the need to complement type-approval metrics with usage-sensitive indicators when evaluating PHEV compliance in practice.

Vehicles doi: 10.3390/vehicles8030059

Authors: Nicolae Filip Calin Iclodean Marius Deac

The COVID-19 pandemic and the resulting mobility restrictions significantly disrupted urban traffic patterns. This study quantitatively assesses the impact of these restrictions on vehicle flow at a signalized central intersection in Cluj-Napoca, Romania, through an integrated methodology combining continuous radar-based traffic measurements and AI (Artificial Intelligence)-assisted video analysis. Traffic data were collected before the pandemic (November 2019) and during the lockdown period (April 2020), enabling a comparative evaluation of flow characteristics and vehicle arrival patterns. Under constrained observational conditions, vehicle arrivals were modeled using a probabilistic framework grounded in Poisson distribution. The findings indicate a dramatic contraction of mobility demand, with traffic volumes declining in 2020 to 9.55% of pre-pandemic levels. The probabilistic assessment highlights the predominance of free-flow regimes under reduced demand and confirms the adequacy of the Poisson model in low-density traffic scenarios. The obtained results contribute to a better understanding of urban traffic dynamics under extreme mobility disruptions and provide a transferable methodological framework for probabilistic traffic modeling, resilience-oriented urban mobility planning, and data-driven traffic management.

Vehicles doi: 10.3390/vehicles8030058

Authors: Shuwan Cui Hao Li Yanzhao Su Jin Huang Kun Cheng Huiqian Li

Mandatory lane-changing in complex driving scenarios poses significant challenges for autonomous driving systems due to complex vehicle interactions and strict safety requirements. Existing methods often rely on handcrafted rules or extensive expert demonstrations, which increase data collection costs and provide limited safety guarantees during learning. To address these issues, this paper proposes a rule-guided reinforcement learning framework for lane-changing policy optimization. A lightweight rule-based controller is employed to generate initial experience, guiding the training of an improved Distributional Soft Actor–Critic with Three Refinements (DSAC-T), while a safety-aware constraint controller filters high-risk actions to ensure stable and safe learning. The proposed method is evaluated in Regular Lane Change and Lane Merging scenarios under mixed traffic composed of aggressive and conservative vehicles within a simulation environment. Simulation results show that although lane-changing success rates decrease as traffic aggressiveness increases, the proposed method consistently outperforms SAC and TD3. Notably, under highly aggressive traffic conditions with an aggressiveness ratio of 0.7, the proposed approach improves the success rate by 17.13% compared to SAC and by 10.49% compared to TD3, demonstrating superior robustness and safety in complex, high-conflict lane-changing scenarios. The present study is conducted solely in simulation and requires further validation before application to real-world traffic environments.

Vehicles doi: 10.3390/vehicles8030057

Authors: Lixue Yan Yingping Hong Lizhi Dang Ruihao Zhang

To address the core challenges of low energy utilization efficiency and limited range in front-wheel-drive electric vehicles (FWD EVs), this study proposes a dynamic series braking energy recovery strategy featuring adaptive braking force distribution and multi-factor correction. A comprehensive simulation model integrating five core modules—Cycle, Driver, Controller, Vehicle, and Display—was developed using Matlab/Simulink, combining the dynamic series recovery strategy with traditional parallel recovery strategies. Model reliability was validated through chassis dynamometer test data (maximum error ≤ 3.2%), followed by simulation comparisons under CLTC conditions. Results demonstrate that compared to parallel strategies, the dynamic series approach increases range by 25.8% (from 318 km to 400 km). Key innovations include real-time adaptive front axle braking coefficients based on braking intensity and a correction mechanism integrating vehicle speed and state of charge (SOC), achieving a balance between recovery efficiency, braking stability, and battery protection. This study provides actionable design guidance for FWD EV powertrain optimization while establishing a validated regenerative braking simulation framework.

Vehicles doi: 10.3390/vehicles8030056

Authors: Zhipeng Gu Xing Wang Yufei Han

Car following is a common and important behavior in vehicle traffic flow, and the fluctuation of car-following behavior caused by the change in weather environment has also become one of the main causes of traffic accidents. To solve this problem, a driving scene on urban roads was built through the driving simulation platform, and the driving simulator was used to carry out the vehicle-following test. The operating behavior parameters of the test drivers, such as steering wheel angle, headway, throttle opening, standard deviation of vehicle speed, acceleration, collision times, and so on, were collected and studied. The results showed that there were significant differences (p < 0.05) in indicators such as steering wheel angle, headway, acceleration, and standard deviation of speed under adverse weather conditions. The bad weather caused the line of sight to be blocked, which the driver compensated for by strengthening the trimming of the steering wheel angle, leading to the deterioration of the vehicle lateral stability. Moreover, safety studies have shown that the minimum driving interval occurred in foggy weather, while the maximum occurred in snowy weather. In addition, the standard deviation of vehicle speed and acceleration fluctuations have been reduced to ensure driving safety in adverse weather conditions. The driving experience of the drivers has a significant impact on the number of collisions, as novice drivers had a higher probability of collision.

Vehicles doi: 10.3390/vehicles8030055

Authors: Mohammed Shabbir Ali Alexis Warsemann Pierre Merdrignac Mohamed-Cherif Rahal Amar Mokrani Wael Jami

Ensuring the safety of Autonomous Driving Systems (ADS) at urban intersections remains challenging due to complex interactions between vehicles and traffic management infrastructure. This study validates an ADS equipped with connected perception using Infrastructure-to-Vehicle (I2V) communication within a combined virtual and hybrid testing approach. The validation follows the overall structure and methodology of the SUNRISE Safety Assurance Framework (SAF), which is applied in detail where required by the scope of the study. Five representative urban intersection scenarios, covering both nominal driving conditions and safety-critical edge cases, are evaluated using virtual simulations in MATLAB/Simulink (2014b) and hybrid experiments integrating OMNeT++ (5.7.1)/Veins (5.2)/SUMO (1.12.0) with real-world components. Key Performance Indicators (KPIs) related to safety, decision-making, longitudinal control, passenger comfort, and V2X communication performance are analyzed. The results show strong consistency between virtual and hybrid testing, with ego vehicle speed deviations below 2 km/h and trigger distance differences under 3 m. V2X communication achieves a near-perfect Cooperative Awareness Message (CAM) delivery ratio, with an average latency of approximately 142 ms. While this latency remains within the tolerance of the deployed ADS, the overall end-to-end delay highlights opportunities for further optimization. The study demonstrates how the SUNRISE SAF can effectively structure ADS validation, identifies critical scenarios such as right-of-way violations by non-priority obstacles, and provides insights into improving connectivity handling and low-speed braking behavior for Cooperative, Connected, and Automated Mobility (CCAM) systems in urban environments.

Vehicles doi: 10.3390/vehicles8030054

Authors: Giacomo Galvanini Massimiliano Gobbi Giampiero Mastinu Carlo Cantoni Raffaello Passoni

This study investigates amplified hydraulic braking systems employed in high-performance motorsport applications, utilizing wedge mechanisms for self-energization. An analytical expression for the gain coefficient is derived from a simplified equilibrium analysis of the wedge-shaped pad, capturing the nonlinear dependency on both wedge angle and effective mean disc-pad friction. A previously validated coupled thermoelastic model for carbon-carbon (C/C) braking systems—developed in Dymola and Modelica using the finite volume method (FVM) and an analytical local friction formulation—is here adapted to wedge-amplified braking systems, with the aim of providing performance assessment during the design phase of new calipers at reduced computational cost compared to coupled thermoelastic finite element method (FEM) models. Several caliper configurations featuring different wedge angles are tested experimentally on a dynamometer. A reduction in the effective friction coefficient at high mean effective contact pressure—induced by pronounced wedge angles and reduced pad areas—is observed. To validate the thermoelastic model, simulated braking torque and disc surface temperature are compared against bench data. The model shows satisfactory predictive capability under various operating conditions and test cycles, with mean error indices on peak torque prediction below 5% for the majority of the simulated cases. Finally, the validated model is used to virtually assess the performance of a new caliper prototype prior to its manufacturing and testing.

Vehicles doi: 10.3390/vehicles8030053

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

Sliding contact wear at the pantograph–catenary interface directly impacts the current collection performance and power supply reliability of electrified railways. Addressing the challenges in multi-environmental wear studies—namely, fragmented modeling chains, inconsistent parameter calibrations, and prohibitive computational costs that hinder horizontal comparisons—this study develops an equivalent parameterized modeling framework tailored for engineering assessment. The framework encapsulates environmental effects as equivalent load increments and interface coefficient corrections, facilitating efficient multi-scenario parameter scanning within a 3D contact model. Findings reveal that environmental factors drive wear through a distinct “pressure-wear” nonlinear decoupling mechanism. In sandy environments, abrasive-mediated micro-cutting dominates, leading to a monotonic surge in wear depth as sand concentration increases, despite a buffered contact pressure response. In icing conditions, the synergy of low-temperature brittleness and geometric impact renders hotspot wear highly sensitive to temperature fluctuations. For salt spray conditions, the environmental impact is represented via equivalent corrections to the interfacial parameters; within this equivalent framework, the results suggest that salt spray intensity has a more pronounced effect on wear accumulation than humidity alone. This work reveals the divergence of dominant damage pathways across environments, offering a quantitative basis for the differentiated maintenance and remaining life estimation of pantograph–catenary systems in extreme climates.

Vehicles doi: 10.3390/vehicles8030052

Authors: Luca Bassani Stefano Lovato Matteo Massaro Nicola Petrone Giuseppe Zullo Matteo Formentini Roberto Lot

The location of the rider centre of mass (CoM) is especially relevant in bicycles and motorcycles due to the large human-to-vehicle mass ratio. This work illustrates two alternative methods for the experimental identification of the longitudinal and lateral coordinates of the rider CoM position as a function of the posture. The first method uses a set of load cells and provides accurate and reliable results. However, riders’ must firmly hold their configuration for the time necessary to stabilise the force measurements, which may be uncomfortable in configurations such as lean-out. The second method utilises an optical system which captures the rider attitude. This information is then used to feed a multibody model, which is used to estimate the CoM coordinates.

Vehicles doi: 10.3390/vehicles8030051

Authors: Jinzhi Feng Guangqi Chen Decheng Wei Chunhui Gong Zujian Wang Xu Long Dongdong Zhang

Braking drag is a typical fault of brake systems, and clarifying the correlation mechanism between vehicular working conditions and braking drag is critical for brake design improvement. Based on fluid mechanics and contact mechanics, this paper establishes a dynamic model for braking drag mechanism analysis, combined with the return mechanism and force-bearing state of brake pistons. Firstly, a commercial vehicle brake system dynamic model is built via Amesim, and piston sliding resistance is identified as the key factor leading to insufficient piston retraction through user operational data analysis. Subsequently, a fluid-structure interaction-based dynamic coupling model of drag mechanism is established, typical braking conditions are extracted via K-means clustering, and piston friction, displacement and drag torque are solved with the system model outputs as inputs. Finally, drag-prone working conditions are determined, and the disc brake drag mechanism is revealed. The results show that piston sliding resistance is the primary factor in braking drag; medium-low speed prolonged braking has high drag susceptibility; and the seal contact area is in mixed lubrication, with contact pressure and friction dominated by asperity shear stress. This work enables accurate identification of drag-prone conditions, providing guidance for brake system optimization.

Vehicles doi: 10.3390/vehicles8030050

Authors: Mousa Abushattal Fadi Alhomaidat Rasha Al-Shamaseen Mohammad Al-Marafi Layan Alkodary Ahmed Jaber

Dynamic Message Signs (DMSs) play a critical role in conveying real-time traffic information to drivers; however, their effectiveness heavily relies on how messages are structured and displayed, particularly regarding phasing duration and content length. This study examines the influence of these two factors on driver readability, comprehension, and gaze behavior using an advanced virtual reality (VR) driving simulator. Controlled experiments simulated four DMS scenarios, combining two phasing intervals (2.5 and 4 s) with short and long message formats, adhering to Michigan Department of Transportation (MDOT) guidelines. The experiment integrated eye-tracking technology to measure fixation duration and frequency, while statistical methods, including survival analysis and LASSO regression, were employed to identify significant predictors of message readability. Results revealed that shorter messages with shorter phasing intervals led to the highest comprehension rates and reduced cognitive strain. Furthermore, individual characteristics such as gender, driving speed, and highway driving experience significantly affected how drivers engaged with DMS messages. These findings contribute to the development of more effective DMS deployment strategies and provide practical design recommendations to enhance traffic safety and information delivery on high-speed roadways.

Vehicles doi: 10.3390/vehicles8030049

Authors: Xiaolong Gong Haiqing Liu Yuehao Wang Yaxin Wei Guoran Shi

Owing to their high-altitude vantage point and maneuverability, unmanned aerial vehicles (UAVs) have emerged as an effective technical solution for real-time parking detection in expressway scenarios. Using UAV cruise-perspective images, this paper proposes an unauthorized parking detection method by analyzing the time-series variations in the relative distances between the moving vehicle and static structure as a reference. Firstly, vehicle and static structure targets are recognized and tracked by the DeepSort, and a Vehicle–Structure (V-S) distance matrix is further constructed to describe their frame-wise relative positions in the pixel coordinate system. Then, to eliminate the radial scale errors caused by perspective distortion, a scale factor (SF) index is introduced to correct the original V-S matrix and provide a more accurate spatiotemporal representation. Finally, the stationarity of the distance series in the V-S matrix is tested using the Augmented Dickey–Fuller (ADF) test, and a parking detection method is proposed by introducing the parking support ratio (PSR) to establish a multi-structure joint decision scheme. Experimental results show that the corrected V-S matrix can faithfully describe the spatial positional relationship between road vehicles and static structures. With the optimal PSR threshold ψ0 and time window T, the proposed method achieves better overall parking-detection performance in terms of accuracy, precision, recall, and F1-score in comparison with a traditional speed threshold approach.

Vehicles doi: 10.3390/vehicles8030048

Authors: Zhengfeng Ma Xuan Wang Jingyi Chen

Traffic congestion not only severely impacts residents’ daily travel quality and increases travel costs, but also triggers traffic accidents, causes environmental pollution, and leads to resource waste. There is a practical need to implement engineering measures simultaneously across multiple intersections to mitigate urban road traffic congestion, which necessitates in-depth research into selecting critical intersection clusters. Based on existing research, the relationship between vehicle emissions and the degree of saturation was derived. The network efficiency evaluation metric was refined using the degree of saturation, and a model linking vehicle emissions to network efficiency was established. A validation experiment was designed using the core road network of Xining City, Qinghai Province, as an example. The results indicate that vehicular exhaust emissions per kilometer are proportional to the saturation degree metric value. The network efficiency metric is inversely proportional to the network’s overall (or average) saturation degree. Vehicular exhaust emissions exhibit an inverse relationship with network efficiency. As the road traffic operational state shifts from congestion to free-flow conditions, for every 1-unit increase in network efficiency value, the average exhaust emissions per vehicle per kilometer decrease by 3.976 kg. Different congestion mitigation node selection schemes correspond to varying total emission reductions during the morning peak. When ranked by the magnitude of increase in network efficiency (from the largest increase to the smallest), the corresponding total morning peak emission reductions gradually decrease in a stepwise manner. According to the C602 and C603 experimental results, compared to the worst node cluster selection scheme, the optimal node cluster selection scheme can reduce vehicular exhaust emissions by 4441 kg and 6616 kg, respectively. These findings provide valuable theoretical and practical insights for implementing energy-saving and emission reduction strategies in urban traffic management.

Vehicles doi: 10.3390/vehicles8030047

Authors: Till Temmen Jasper Debougnoux Li Li Björn Krautwig Tobias Brinkmann Markus Eisenbarth Jakob Andert

The development of AI-driven automated driving functions requires vast amounts of diverse, high-quality data to ensure road safety and reliability. However, both the manual collection of real-world data and creation of 3D environments are costly, time-consuming, and hard to scale. Most automatic environment generation methods still rely heavily on manual effort, and only a few are tailored for Advanced Driver Assistance Systems (ADAS) and Automated Driving Systems (ADS) training and validation. We propose an automated generative framework that learns ground-truth features to reconstruct 3D environments from a road definition and two simple parameters for country and area type. Environment generation is structured into three modules—map-based data generation, semantic city generation, and final detailing. The overall framework is validated by training a perception network on a mixed set of real and synthetic data, validating it solely on real data, and comparing performance to assess the practical value of the environments we generated. By constructing a Pareto front over combinations of training set sizes and real-to-synthetic data ratios, we show that our synthetic data can replace up to 85% of real data without significant quality degradation. Our results demonstrate how multi-layered environment generation frameworks enable flexible and scalable data generation for perception tasks while incorporating ground-truth 3D environment data. This reduces reliance on costly field data and supports automated rapid scenario exploration for finding safety-critical edge cases.

Vehicles doi: 10.3390/vehicles8030046

Authors: Arkadiusz Kampczyk Anna Woźnica-Hanusik Tomasz Iwan

Dangerous goods transport (DGT) is of strategic importance for any economy, and the structure of the fuel and energy industry includes a number of systems and facilities qualified as “critical infrastructure” (CI). Given the current geopolitical situation, sabotage, hybrid or even terrorist activities in the area of logistics and transport pose an increasing threat. At the same time, next to the economic sector, liquid fuels are of great importance to citizens, which is why the transport of this group of goods should be given special importance, ensuring appropriate efficiency and safety parameters, taking into account the risk of intentional, destructive human interference. A significant source of data in the road transport of dangerous goods is the spatial data infrastructure (SDI); digital interactive platforms (DIP) are important here. This scientific research work concerns the application of DIP and related information technologies (IT) in road transport—smart mobility (SM). The main objective of the scientific research work is to develop proposals for effective tools to minimize the overall risk, using publicly available digital interactive platforms. In the implementation of the topic, the following methods were integrated: OKR (Objectives and Key Results), SMART (Specific, Measurable, Achievable, Relevant, Time-bound) and CS (Case Study). The main problem was identified and the main goal of the work was achieved. The results made it possible to present effective risk minimization tools in DGT using DIP. The elaboration was prepared under the research subvention of the AGH University of Krakow, No. 16.16.150.545 in 2026.

Vehicles doi: 10.3390/vehicles8030045

Authors: Shichao Han Yun Bai Yao Chen

Heavy-haul railways are important for bulk freight transport, and improving their transport capacity is essential for railway operators to enhance operational efficiency. This study develops an integer linear programming model for train formation planning that maximizes transport capacity, incorporating key practical constraints such as section headway, station capacity, and locomotive matching. This study makes two main contributions: (1) explicit formulation of transport-capacity maximization as the primary objective; and (2) incorporation of specific train formation rules through linear resource-flow coefficients that characterize the combination and decomposition operations. The model is applied to the Shuozhou–Huanghua Railway in a case study. Experimental results show that the optimized train formation plan increases total freight volume from 2810.4 thousand tons to 3080.0 thousand tons, representing a capacity improvement of approximately 9.6%. This result is achieved by adjusting the mix of train tonnage levels, increasing combination operations for medium-capacity trains, and reallocating locomotive types in accordance with traction requirements. The study demonstrates that a capacity-oriented optimization framework can effectively support train-formation plan decisions under practical operational constraints, providing railway operators with a systematic tool to enhance line utilization without expanding infrastructure.

Vehicles doi: 10.3390/vehicles8030044

Authors: Dan Alexandru Micu Marius Valentin Bățăuș Cristian Alexandru Rențea Alexandru Adrian Ancuța Robert Mancaș

This study examines how longitudinal road gradients affect the energy consumption and driving range of a Tesla electric vehicle using dynamometer measurements and Simulink simulations. Tests performed on slopes from 0% to 4% show a strong inverse relationship between gradient and range, with more than a 62% reduction at a 4% incline. The Simulink model accurately reproduces these trends despite the tested vehicle’s age and battery degradation. Shifting from driving range to energy consumption metrics provides a more robust assessment of vehicle efficiency, revealing that uphill segments substantially increase consumption, while downhill segments enable significant recuperation. When averaged, these effects nearly cancel out for moderate slopes, especially at higher speeds where aerodynamic drag dominates. Constant-speed simulations confirm that slope has minimal net impact at highway speeds but strongly affects consumption at urban speeds, with increases of up to 17% at a 4% gradient. Overall, the findings highlight road gradients as a key factor in EV energy modelling and emphasize the need to incorporate terrain and driving environment into predictive range estimation and eco-routing strategies.