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

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.

Vehicles doi: 10.3390/vehicles8020043

Authors: Julian Borowski Hinrich Emsmann Jannis Kneule Rico Ruess Stephan Rudolph

Increasing product complexity, shorter development cycles and cross-domain integration demands pose significant challenges for modern race car engineering teams. In Formula Student teams, heterogeneous toolchains, manual data exchange, late system integration, and high personnel turnover hinder efficient collaborative development and lead to repeated knowledge loss. This paper presents an integrated digital-engineering framework combining graph-based design languages (GBDL), model-to-text transformations, natural-language interactions via Large Language Models (LLMs), and Git-based version control to address these issues. By formalizing design knowledge and storing it in a centralized design graph, the framework ensures digital consistency of data and models, supports automated vehicle design variant generation, and enables seamless cross-domain integration. Through case studies of three Formula Student teams, the methodology demonstrates quantifiable reductions in design iteration time, enabling the evaluation of more than 104 suspension variants within days instead of a few dozen manually created variants, while reducing hands-on engineering effort from minutes per variant to a largely unattended optimization process. The results indicate that the approach not only enhances efficiency and collaboration but also preserves design knowledge for long-term knowledge management and reuse. Looking forward, this methodology provides a scalable route toward further engineering automation, systematic variant-driven development, and early-stage design optimization supported by design languages and integrated downstream toolchains.

Vehicles doi: 10.3390/vehicles8020042

Authors: Jaber Ouakrim Abdoulaye Bodian Dina Ouardani Alben Cardenas

Brushless DC (BLDC) motors are widely used in mobile robotics and off-road vehicles due to their high efficiency, reliability, and compactness. However, achieving robust, high-performance speed control in embedded environments remains challenging due to nonlinearities, dead-time effects, parameter uncertainties, and strict real-time constraints. This paper presents a comprehensive experimental study of classical and robust control strategies for BLDC motor speed control, fully implemented on an FPGA platform. Classical PI and PID controllers tuned using Ziegler–Nichols, Cohen–Coon, and Chien–Hrones–Reswick methods are first investigated and discretized using both Zero-Order Hold (ZOH) and Tustin (bilinear) approximations. Model-based approaches, including IMC-based PID controllers, are then introduced to enhance robustness. In addition, a robust two-degree-of-freedom dead-time compensator (DTC) is implemented to explicitly address dead-time uncertainties inherent to inverter-based motor drives. All controllers are implemented using fixed-point arithmetic on a Xilinx Nexys A7 FPGA and validated experimentally on a BLDC motor test bench representative of semi-autonomous robotic applications. Performance is evaluated through time-domain responses and quantitative indices, including ISE, ITAE, I, control effort, and FPGA resource utilization. Experimental tests under controlled DC bus voltage disturbances are conducted to assess disturbance rejection capability and robustness under realistic operating conditions. Experimental results demonstrate that Tustin discretization consistently improves tracking performance, while IMC-PID and DTC strategies provide superior robustness against dead-time and modeling uncertainties, making them particularly suitable for embedded FPGA-based motor control.

Vehicles doi: 10.3390/vehicles8020041

Authors: Eugen Valentin Butilă Gheorghe-Daniel Voinea Răzvan Gabriel Boboc Grigore Ambrosi

Autonomous vehicles (AVs) are expected to significantly influence road safety, traffic efficiency, and urban mobility. However, their real-world impacts depend not only on vehicle-level automation but also on interactions within the broader traffic ecosystem, including human-driven vehicles, vulnerable road users, infrastructure, and governance frameworks. This review provides a system-level synthesis of recent research on the integration of autonomous and connected autonomous vehicles in mixed traffic environments. Following PRISMA 2020 guidelines, 51 peer-reviewed studies published between 2016 and 2025 were systematically reviewed and thematically analyzed. The review addresses technological foundations, safety impacts, traffic flow and network performance, mixed traffic dynamics, infrastructure and urban systems, and policy and governance challenges. The findings indicate that AV impacts are highly non-linear and sensitive to market penetration rates, control strategies, and human behavioral adaptation. While high levels of automation and connectivity can improve safety, capacity, and traffic stability, early-stage deployment may temporarily increase delays and traffic conflicts. Policy measures—such as pricing, shared mobility integration, and regulatory oversight—are therefore critical to ensuring that AV deployment delivers sustainable and equitable mobility outcomes.

Vehicles doi: 10.3390/vehicles8020040

Authors: Xiaozheng Wang Pak Kin Wong Hengli Qi Shiron Thalagala Ziqi Yang Jingyu Lu Wei Huang

Tire blowouts represent one of the most hazardous fault scenarios for electric vehicles (EVs). While collaborative active steering control (ASC) and direct yaw moment control (DYC) can theoretically maintain stability during these events, the strong coupling effects between them make controller design challenging. To address this, an adaptive control algorithm based on broad-extreme reinforcement learning (RL), named broad critic extreme actor (BCEA), is proposed. Compared to traditional controllers, the proposed BCEA architecture is simpler to design and demonstrates enhanced robustness. Crucially, it achieves significantly faster training speed than traditional RL methods such as deep deterministic policy gradient (DDPG). Both simulation and scaled prototype tests verify the ability of the BCEA-based controller to maintain vehicle stability during different types of tire blowout scenarios. Furthermore, compared to traditional RL methods, the training efficiency is improved by more than 80%. These results indicate that the proposed BCEA controller is a promising advancement for vehicle stability control under critical failure conditions.

Vehicles doi: 10.3390/vehicles8020039

Authors: Fatemeh Ghorbani Augustin Hym Mohammed Elhenawy Andry Rakotonirainy

Motorcyclists face a disproportionately high risk of severe injury or death compared to other road users, highlighting the need for intelligent rider assistance technologies. This paper presents an initial, modular, and interpretable AI pipeline that generates context-aware safety advice from first-person motorcycle videos with practical inference latency suitable for on-device deployment, framing large language models as interpretable cognitive support agents for motorcycle safety. The system integrates lightweight perception and reasoning components to emulate the function of an Advanced Rider Assistance System (ARAS). Video frames are processed at 1 FPS using Pixtral, a Mistral-based multimodal large language model (MLLM), to produce descriptive scene captions, while YOLOv8 identifies key objects such as vehicles, pedestrians, and road hazards. A Mistral-small language model then fuses this information to generate concise, imperative safety tips. Preliminary evaluations on publicly available motorcycle POV datasets demonstrate promising performance in terms of contextual accuracy, interpretability, and scalability, suggesting potential for real-world deployment in low-resource or embedded environments. The proposed framework offers interpretable, context-aware safety assistance that is particularly valuable for young and newly licensed riders during the transition from supervised training to independent riding, where real-time hazard interpretation support is most needed.

Vehicles doi: 10.3390/vehicles8020038

Authors: Jiashuo Xu Chunguang He Ya Duan Yazan Mualla Mahjoub Dridi Abdeljalil Abbas-Turki

Decarbonizing the transport sector is crucial for achieving global carbon peaking and carbon neutrality goals. Electric taxis (e-taxis), which play a vital role in urban public transportation, are central to this transition. However, their operational performance deteriorates significantly under extremely cold conditions. Existing planning models for charging infrastructure often overlook the impact of low temperatures, creating a critical research gap. To address this issue, we propose a novel planning framework using Urumqi, China (43.8° N, 87.6° E) as a case study. Urumqi is a major cold-region metropolis, where January temperatures regularly drop below −20 °C. Our methodology includes two key steps: integrating 412 driver questionnaires and 1.2 million high-resolution GPS trajectories to extract temperature-sensitive charging demand profiles; and incorporating these profiles into an integer linear programming (ILP) model to minimize lifecycle costs, considering climatic constraints, taxi operation patterns, and grid limitations. A key innovation is a temperature-correction coefficient, which dynamically adjusts vehicle energy consumption and driving range based on ambient temperature. Results show superiority over conventional (temperature-ignoring) and random plans: 14-fold lower annualized cost, 23-fold shorter average queuing time, 96.2% high-frequency demand coverage (+16.6%), and 78% charging station utilization (+50.0%). It achieves 29.8–32.3% cost savings at −5 °C (over 25.9% even at −35 °C) and scales stably for 5–50% e-taxi penetration, offering a transferable framework for cold-region e-taxi charging optimization.

Vehicles doi: 10.3390/vehicles8020037

Authors: Ibrahima Ka Ansoumana Noumou Djité Seynabou Anna Chimére Diop Godwin Kafui Ayetor Boucar Diouf

The global shift toward electric mobility represents a cornerstone of sustainable energy transitions; however, developing countries face distinct structural, economic, and infrastructural challenges that constrain their participation in this transformation. This paper examines the conditions, policy frameworks, and infrastructural requirements necessary for a successful electric vehicle (EV) transition in developing countries, with particular attention to the interplay between energy access, transportation policy, and grid readiness. Using a mixed-methods approach that integrates policy analysis, partial life-cycle assessment (LCA) with the second-hand market, and case studies across sub-Saharan Africa and South Asia, the study evaluates the implications of limited electricity access, unreliable power grids, and the dominance of informal transport systems on EV adoption. The findings reveal that, while EVs offer significant potential for reducing emissions and improving urban air quality, their deployment depends critically on coordinated investments in renewable-based electricity generation, charging infrastructure, and supportive regulatory frameworks. Policy strategies such as fiscal incentives, public–private partnerships, and decentralized charging networks can accelerate uptake when aligned with energy-access goals. The paper argues that the EV transition in developing economies must be policy-driven and context-adapted, integrating mobility electrification with broader agendas of energy justice, rural electrification, and industrial development. Ultimately, the research provides a roadmap for aligning electric mobility policies with sustainable infrastructure development to ensure that the global EV revolution becomes both inclusive and equitable.

Vehicles doi: 10.3390/vehicles8020036

Authors: Muhammad Shoaib Hanif Zubair Nawaz Muhammad Kamran Malik

Intelligent transportation systems (ITSs) are crucial for modern traffic management and law enforcement. This paper addresses the challenge of monitoring and managing extensive vehicle traffic in large cities like Lahore, Pakistan. We propose a deep learning based ITS utilizing Vision Transformers combined with convolutional feature extraction to accurately identify vehicle type, color, make/model, and license plates. Experiments were conducted on a comprehensive dataset collected from multiple checkpoints across Lahore under varying environmental conditions. Our proposed model achieved high accuracy rates: 98.0% for vehicle type classification, 96.0% for color detection, 95.0% for make/model identification, and 89.0% for license plate recognition. These results demonstrate the system’s potential to significantly enhance traffic management and road safety and support law enforcement operations in developing urban environments.

Vehicles doi: 10.3390/vehicles8020035

Authors: Fabiana Carrión Gregorio Romero Jose-Manuel Mira Jesus Félez

This paper presents a hybrid qualitative–quantitative framework for assessing the technical feasibility and system readiness of emerging multimodal mobility concepts, with specific application to the Pods4Rail project. The methodology integrates expert-based Technology Readiness Level (TRL) assessment with a probabilistic System Readiness Level (SRL) estimation that incorporates uncertainties in both TRLs and Integration Readiness Levels (IRLs). The qualitative component uses expert judgment and visual heat maps to identify subsystem-specific maturity gaps, particularly in automation, digitalization, and sustainability. The quantitative component explicitly separates three methodological layers often treated implicitly in prior research: (i) the probabilistic model representing uncertainties in TRL and IRL, (ii) the uncertainty-propagation problem linking these variables to system-level readiness, and (iii) the Monte Carlo algorithm employed to solve this problem. This structure enables the derivation of SRL distributions that reflect uncertainty more realistically than deterministic approaches, allowing statistical analysis of different characteristics of these distributions and exploratory sensitivity analysis. Results show that the Pods4Rail system is positioned between SRL 1 and SRL 2, corresponding to concept refinement and technology development stages. While hardware-related subsystems such as the Transport Unit and Rail Carrier Unit exhibit relatively higher maturity, planning, logistics, and operational management functionalities remain at early development stages. By combining interpretative insight with statistical rigor, the proposed framework offers a transparent and reproducible approach to early-phase readiness assessment. Its transferability makes it suitable for other innovative mobility systems facing similar challenges of incomplete information, uncertain integration pathways, and high conceptual complexity.

Vehicles doi: 10.3390/vehicles8020034

Authors: Shengqi Huang Yuqing Huang Le Wang Lei Shi Junwu Zhang

In electric vehicle and marine propulsion applications, the stable operation of permanent-magnet synchronous motor (PMSM) drive systems relies on accurate rotor position information. Such information is typically obtained from position sensors, which are prone to high temperature, humidity, vibration, and electromagnetic interference, leading to elevated failure rates; moreover, sensor installation introduces additional interfaces and wiring, thereby reducing system reliability. To address these issues, this paper proposes a sensorless control method based on an adaptive full-order sliding-mode observer (SMO). The proposed method employs the SMO output as the observer feedback correction term rather than the estimated back EMF, thereby avoiding substantial high-frequency noise. Furthermore, an S-shaped nonlinear function is designed to replace the conventional switching function, mitigating high-frequency chattering when the system operates in sliding mode; an adaptive sliding-mode gain function is designed, the sliding-mode gain and the boundary-layer thickness are adaptively tuned as a function of motor speed, which effectively enhances the back EMF estimation accuracy over a wide operating-speed range. The effectiveness of the proposed method is validated on a 2.3-kW PMSM experimental platform.

Vehicles doi: 10.3390/vehicles8020033

Authors: Basheer Wasef Shaheen Ahmed Jaber

Electric scooters and other micromobility innovations are becoming standard fare in urban transportation networks. Yet there are several obstacles that must be overcome, including concerns about users’ satisfaction and safety. This study aimed primarily at developing a user-centered methodological framework that combined different user-centered engineering tools such as voice of customers analysis, needs–metrics mapping, Pugh’s matrix and morphological design, strategic analysis approaches such as SWOT and PESTEL, and, a key innovation, the smart terrain-adaptive power management system (STAPMS), an AI-based feature that dynamically adjusts power output and regenerative braking based on Budapest’s varied topography and road conditions to improve energy efficiency and ride comfort. This innovative framework offers insights into redesign options aimed at enhancing customer satisfaction, product quality, and business growth. The proposed framework was validated on Lime electric scooters, particularly the S2 generation type. Three design concepts were generated and evaluated through a systematic approach to provide an optimal balance between users’ needs, technical performance, and strategic feasibility. The proposed user-centered framework shows significant potential to improve users’ satisfaction, enhanced usability, extended range, and increased market competitiveness, validating its viability for micromobility innovative solutions. The findings also demonstrate the necessity for systematic frameworks that link user experience with engineering design and can be generalized to other micromobility products.

Vehicles doi: 10.3390/vehicles8020032

Authors: Muhammad Hussain Muladilijiang Baikejuli Jing Shi Amjad Pervez Matthew A. Albrecht Etikaf Hussain Razi Hasan Teresa Senserrick

The driving simulator’s ability to provide practical, safe, and controlled environments has made it a widely used tool for evaluating driving behaviours in the realm of road safety. To consolidate the fragmented research in this area, this study is divided into two parts: a bibliometric analysis and a narrative review: (a) the bibliometric analysis identified 4992 studies, expanding from 2000 to June 2025, sourced from four databases—Web of Science, Scopus, TRID, and Google Scholar (supplementary)—and examined trends over the years, the general topics covered, the countries where studies were conducted, and the main research fields associated with driving simulators; and (b) the narrative review further analysed 48 selected studies from eight domains (distraction, fatigue and drowsiness, traffic-calming measures, impairment from psychoactive drugs, road curves, intersections, tunnels, and adverse weather conditions) to provide insights into how driving simulators have contributed to these fields, the methodologies employed by researchers, and the practical applications of the findings. The study aims to provide clear and essential insights for new and emerging researchers, offering an accessible overview of how driving simulators have evolved, why they are important, how they measure different driving metrics, and how they ultimately improve road safety. The findings indicate that driving simulator studies are increasingly prominent in research on driver behaviour (e.g., driving speed, lateral movement, and acceleration/deceleration).

Vehicles doi: 10.3390/vehicles8020031

Authors: Wuyang Yuan Zhen Ren Zhongrui Zhou Yu Ke

This study explores strategies for dynamic pricing and flexible refund fee setting in railway line services, aiming to optimize ticket sales revenue by integrating refund mechanisms into the revenue management framework. By introducing a consistent concept of opportunity cost applicable to both passengers and railway operators, we propose an integrated approach that combines dynamic pricing with flexible refund fees grounded in the demand-driven opportunity cost of seat resources. A dynamic programming model is constructed to quantify the opportunity cost of seat resources. To address the computational challenges arising from the model’s scale, state and time dimension compression methods are applied to develop an approximate linear programming model with fewer constraints. The proposed model is solved using a turning point search algorithm and a constraint generation algorithm. Numerical experiments and ticket sales simulations are conducted to verify the feasibility of the proposed methods and to explore the application effects of different pricing strategy combinations. The results demonstrate that the integration of dynamic pricing and flexible refund fees can significantly enhance ticket sales revenue, particularly in scenarios of supply shortfall.

Vehicles doi: 10.3390/vehicles8020030

Authors: Antonio Comi Eskindir Ayele Atumo Elsiddig Elnour

Vehicle-to-grid (V2G) services are gaining attention as a strategy to integrate electric vehicles (EVs) into sustainable energy systems. Although technological aspects have been widely studied, methodologies for identifying optimal V2G hubs and forecasting the energy available for grid transfer remain limited. This study introduces a data-driven approach to (i) identify the optimal V2G region based on the aggregated parking duration using floating car data (FCD; collected from GPS-enabled vehicles); (ii) estimate the surplus battery capacity of electric vehicles in that region; and (iii) forecast the energy transferable to the grid. The methodology applies spatial k-means clustering to define candidate zones, computes aggregated parking durations, and selects the optimal hub. The surplus energy is estimated considering the daily mobility needs of users, 20% reserve, and transfer rates. For forecasting, autoregressive integrated moving average (ARIMA) and long short-term memory (LSTM) models are implemented and compared. The proposed methodology has been applied to a real case study, using 58 days of FCD observations. The empirical findings of this study show the goodness of the proposed methodology, and the opportunity offered V2G technology to support the sustainable use of energy. The ARIMA model demonstrated a superior forecasting performance with an RMSE of 52.424, MAE of 36.05, and MAPE of 12.98%, outperforming LSTM (RMSE of 99.09, MAE of 80.351, and MAPE of 53.20%) under the current data conditions. The results of this study suggest that for supporting grid needs of a medium-sized city, V2G plays a key role, and at the current status of the EV penetration, the use of FCD and predictive approaches is paramount for making an informed decision.

Vehicles doi: 10.3390/vehicles8020029

Authors: Hugo Raposo Jorge Raposo José Torres Farinha J. Edmundo de-Almeida-e-Pais

Passenger transport companies have often been affected by fires in their vehicles, causing considerable damage. As a result, it is important to study the causes and effects of these fires, as well as to define the maintenance policies and strategies to be implemented to minimize the probability of this type of accident occurring. The support for this paper was based on the study of an accident that occurred in Portugal involving a passenger bus that suffered a fire in the engine compartment, which spread to the passenger compartment and caused the destruction of the vehicle, with no personal injuries. This study used infrared image analysis technology, oil ignition temperature analysis, maintenance history, accident history and operator interviews to determine the possible cause of the ignition. It was found that the cause was due to oil leaks from the engine compartment cooling system. The present communication will share a set of explanatory elements of the circumstances in which the accident occurred. In addition to identifying the causes of the accident, the study warns of the importance of more effective and efficient maintenance, particularly when using Condition Based Maintenance (CBM), including periodic visual inspections of the various mechanical and electrical components that make up the vehicles. The conclusions presented in the study also show that these events are not unrelated to the poor or even non-existent maintenance policy for the entire fleet, including the applicable standards.

Vehicles doi: 10.3390/vehicles8020028

Authors: Marcel Simeonov Andrei Kurdiumov Milan Dado

Accurate depth perception is vital for autonomous driving and roadside monitoring. Traditional stereo vision methods are cost-effective but often fail under challenging conditions such as low texture, reflections, or complex lighting. This work presents a perception pipeline built around FoundationStereo, a Transformer-based stereo depth estimation model. At low resolutions, FoundationStereo achieves real-time performance (up to 26 FPS) on embedded platforms like NVIDIA Jetson AGX Orin with TensorRT acceleration and power-of-two input sizes, enabling deployment in roadside cameras and in-vehicle systems. For Full HD stereo pairs, the same model delivers dense and precise environmental scans, complementing LiDAR while maintaining a high level of accuracy. YOLO11 object detection and segmentation is deployed in parallel for object extraction. Detected objects are removed from depth maps generated by FoundationStereo prior to point cloud generation, producing cleaner 3D reconstructions of the environment. This approach demonstrates that advanced stereo networks can operate efficiently on embedded hardware. Rather than replacing LiDAR or radar, it complements existing sensors by providing dense depth maps in situations where other sensors may be limited. By improving depth completeness, robustness, and enabling filtered point clouds, the proposed system supports safer navigation, collision avoidance, and scalable roadside infrastructure scanning for autonomous mobility.

Vehicles doi: 10.3390/vehicles8020026

Authors: Hongjie Cheng Zhenwei Hou Zhihao Liu Jianhua Li Jiashuo Zhang Yuan Zhao Xiuyu Liu

Multi-axle vehicles are commonly used for heavy-duty special operations, which easily leads to high driving torque demands when adopting distributed electric drive configurations. This study achieves the objective of reducing the driving torque of each in-wheel motor while controlling the stability of multi-axle vehicles. Taking a five-axle distributed drive test vehicle as the research object, a hierarchical control strategy integrating active all-wheel steering and direct yaw moment control is proposed. The upper layer is implemented based on model predictive control, with fuzzy control introduced to dynamically adjust control weights; the lower layer accomplishes the allocation of targets calculated by the upper layer through minimizing the objective function of tire load ratio. A linear parameter varying (LPV) tire model is introduced into the vehicle model to improve the calculation accuracy of tire lateral forces, and a neural network method is employed to solve the real-time performance issue of the model predictive control (MPC) controller. The proposed strategy is verified through a combination of simulation and real vehicle tests. High-speed condition simulations demonstrate that the AWS/DYC strategy significantly outperforms the ARS/DYC approach: compared to the active rear-wheel steering strategy, while the sideslip angle is reduced by 90.98%, the peak driving torque is reduced by 30.78%. Notably, tire slip angle analysis reveals that AWS/DYC maintains relatively uniform slip angle distribution across axles with a maximum of 4.7°, entirely within the linear working region, optimally balancing tire performance utilization with lateral stability while preserving safety margin, whereas ARS/DYC causes slip angles to exceed 11.9° at the rear axle, entering saturation. Low-speed real vehicle tests further confirm the engineering applicability of the strategy. The proposed method is of significant importance for the application of distributed drive configurations in the field of special vehicles.

Vehicles doi: 10.3390/vehicles8020027

Authors: Xiaopeng Yuan Haoyu Wang Lei Zhang

This paper presents a semi-circular, non-contact current sensor designed to simplify the layout of automotive wiring harnesses and enhance measurement convenience and reliability. The sensor integrates a hybrid sensing array consisting of Hall-effect and tunnel magnetoresistance (TMR) elements. To address common challenges in automotive power systems and vehicle wiring—such as conductor eccentricity and magnetic interference from adjacent cables—two key techniques are proposed. First, an eccentricity error compensation algorithm is developed, achieving a measurement accuracy of 97.07% under specific misalignment conditions. Second, an equivalent modeling method based on eccentricity principles is introduced to characterize interference fields in complex wiring environments, maintaining 94.31% accuracy in the presence of external disturbances. When the conductor is centered within the array, the average measurement accuracy reaches 99.05%. Experimental results demonstrate that the proposed sensor can reliably measure large currents from 0 to 210 A, making it highly suitable for applications in electric vehicles, high-voltage harness monitoring, power electronics, and intelligent transportation systems.

Vehicles doi: 10.3390/vehicles8020025

Authors: Xiaoyang Li Tao Chen Lebin Zhao Yang Luo Pengfei Zhang Meng Wang

This study was conducted in order to enrich the safety evaluation system of vehicles on complex road sections and provide quantitative support for speed control and driving decision-making. To address the driving stability issue caused by trajectory uncertainty on curved roads, we analyzed lane-changing stability and found that trajectory variations induce a step change in centrifugal force, aggravating lateral instability. Secondly, we developed a variety of simulation schemes to determine the stability limit speed under multi-source information fusion and constructed the corresponding database. Finally, we established an interpretable driving stability evaluation method based on the Differential Evolution-Extended Belief Rule Base-Shapley Additive Explanations (DB-EBRB-SHAP) model. This model incorporates driving behavior as a qualitative variable into the hybrid framework, and its accuracy was further enhanced through parameter optimization. The results demonstrate that the model achieves high evaluation accuracy for driving stability on curved road sections (MAE = 0.0306 and RMSE = 0.0363). Interpretability analysis reveals that curve radius and lane-changing behavior are the key influencing parameters; the negative interaction effect between the two on driving stability will weaken as the curve radius increases.

Vehicles doi: 10.3390/vehicles8010024

Authors: Julian Borowski Stephan Rudolph

The development of modern vehicles faces an increase in complexity, as well as a need for shorter development cycles and a seamless cross-domain integration. In order to meet these challenges, a graph-based design language which formalizes and automates engineering workflows is presented and applied in a design case study to a Formula Student race car suspension system. The proposed method uses an ontology-based vocabulary definition and executable model transformations to compile design knowledge into a central and consistent design graph. This graph enables the automatic generation of consistent 3D CAD models, domain-specific simulations and suspension kinematic analyses, replacing manual and error-prone tool and data handover processes. The design language captures both the structural and dynamic behavior of the suspension, supports variant exploration and allows for integrated validation, such as 3D collision detection. The study illustrates how graph-based design languages can serve as ‘digital DNA’ for knowledge-based product development, offering a scalable, reusable platform for engineering automation. This approach enhances the digital consistency of data, the digital continuity of processes and the digital interoperability of tools across all relevant engineering disciplines in order to support the validation of early-stage designs and the optimization of complex systems.

Vehicles doi: 10.3390/vehicles8010023

Authors: Hakam Bataineh Dmitry Manasreh Munir Nazzal Ala Abbas

This study focused on developing and validating machine learning models to predict pavement marking retroreflectivity using Light Detection and Ranging (LiDAR) intensity data. The retroreflectivity data was collected using a Mobile Retroreflectometer Unit (MRU) due to its increasing acceptance among states as a compliant measurement device. A comprehensive dataset was assembled spanning more than 1000 miles of roadways, capturing diverse marking materials, colors, installation methods, pavement types, and vehicle speeds. The final dataset used for model development focused on dry condition measurements and roadway segments most relevant to state transportation agencies. A detailed synchronization process was implemented to ensure the accurate pairing of retroreflectivity and LiDAR intensity values. Using these data, several machine learning techniques were evaluated, and an ensemble of gradient boosting-based models emerged as the top performer, predicting pavement retroreflectivity with an R2 of 0.94 on previously unseen data. The repeatability of the predicted retroreflectivity was tested and showed similar consistency as the MRU. The model’s accuracy was confirmed against independent field segments demonstrating the potential for LiDAR to serve as a practical, low-cost alternative for MRU measurements in routine roadway inspection and maintenance. The approach presented in this study enhances roadway safety by enabling more frequent, network-level assessments of pavement marking performance at lower cost, allowing agencies to detect and correct visibility problems sooner and helping to prevent nighttime and adverse weather crashes.

Vehicles doi: 10.3390/vehicles8010022

Authors: Bauyrzhan Sarsembekov Madi Issabayev Nursultan Zharkenov Altynbek Kaukarov Isatai Utebayev Akhmet Murzagaliyev Baurzhan Zhamanbayev

Vehicle exhaust gases remain one of the key sources of atmospheric air pollution and pose a serious threat to ecosystems and public health. This study presents an experimental investigation into reducing the toxicity of gasoline internal combustion engine exhaust using ultrasonic waves and infrared (IR) laser exposure. An original hybrid system integrating an ultrasonic emitter and an IR laser module was developed. Four operating modes were examined: no treatment, ultrasound only, laser only, and combined ultrasound–laser treatment. The concentrations of CH, CO, CO2, and O2, as well as exhaust gas temperature, were measured at idle and under operating engine speeds. The experimental results show that ultrasound provides a substantial reduction in CO concentration (up to 40%), while IR laser exposure effectively decreases unburned hydrocarbons CH (by 35–40%). The combined treatment produces a synergistic effect, reducing CH and CO by 38% and 43%, respectively, while increasing the CO2 fraction and decreasing O2 content, indicating more complete post-oxidation of combustion products. The underlying physical mechanisms responsible for the purification were identified as acoustic coagulation of particulates, oxidation, and photodissociation of harmful molecules. The findings support the hypothesis that combined ultrasonic and laser treatment can enhance real-time exhaust gas purification efficiency. It is demonstrated that physical treatment of the gas phase not only lowers the persistence of by-products but also promotes more complete oxidation processes within the flow.

Vehicles doi: 10.3390/vehicles8010021

Authors: Kaplan Ugur Bulut Hamid Mostofi

This study investigates cross-cultural differences in public perception of mobility electrification by applying natural language processing (NLP) techniques to social media discourse in Germany and China. Using a large language model (LLM), this study conducted sentiment analysis and zero-shot text classification on over 10,000 posts to explore how citizens in each country engage with the topic of electric mobility. Results reveal that while infrastructure readiness is a dominant concern in both contexts, German discourse places greater emphasis on environmental impact, often reflecting skepticism toward sustainability claims. On the other hand, Chinese discussions highlight technological advancement and infrastructure expansion, with comparatively limited focus on environmental concerns. These findings show the importance of culturally tailored policy and communication strategies in supporting the public acceptance of electric mobility. By demonstrating how artificial intelligence-driven large-scale social media data analysis can be used to analyze public sentiment across linguistic and cultural contexts, this study contributes methodologically to the emerging field of computational social science and offers practical insights for mobility policy in diverse national settings.

Vehicles doi: 10.3390/vehicles8010020

Authors: Jianuo Hao Fengze Fan Xin Fu

Predicting the severity of traffic accidents remains challenging due to the limited ability of existing methods to extract deep semantic information from unstructured accident narratives, as traditional approaches typically depend on structured data alone. This study proposes a severity prediction approach enhanced by semantic risk reasoning derived from large language models (LLMs). A prompt-engineering template is designed to guide LLMs in extracting proxy semantic features from accident descriptions, forming an enriched feature set that incorporates causal logic. These semantic features are fused with traditional structured features through three integration strategies—direct feature concatenation, optimized feature selection, and model-level fusion. Experiments based on 4013 accident records from expressways in Yunnan Province, China, demonstrate that models using LLM-derived semantic features significantly outperform those relying solely on structured features. Notably, the LightGBM model utilizing semantic features within a balanced learning framework achieves a severe accident recall of 77.8%. While model-level fusion proves optimal for XGBoost (improving Macro-F1 to 0.6356), we identify a “feature dilution” effect in other classifiers, where high-quality semantic reasoning is compromised by low-quality structured noise. These findings indicate that the proposed approach effectively enhances the identification of high-risk accidents and offers a novel semantic-aware solution for traffic safety management. Furthermore, the obtained results provide actionable insights for traffic management agencies to optimize emergency response resource allocation and formulate targeted accident prevention strategies.

Vehicles doi: 10.3390/vehicles8010019

Authors: Radu Tarulescu Stelian Tarulescu Razvan Gabriel Boboc Mircea Nastasoiu

This research paper proposes a solution to reduce CO2 emissions from a spark ignition engine’s exhaust gases by installing a filtration system on the vehicle’s exhaust pipe. The analyzed filtration system was not patented and was in the testing stage. Tests will also be carried out on the stand. The tested system can be used to reduce CO2 levels in automotive exhaust gases and for static applications (generators, internal combustion engine test stands, fossil fuel power generation systems). The need for a system to reduce pollutant emissions emerged with the average age in Europe. In proper conditions, some vehicles can use this type of filtration system. The tested vehicle is a vehicle (produced in 2009) equipped with a 75HP Spark Ignition Engine. The CO2 filtration system consists of a container containing a reactive aqueous solution comprising water, CaO, and MgO. Four tests were performed: the first without a filter, and the other three with the filter placed at different distances from the exhaust pipe end to the reactive solution surface. The tests consisted of evaluating the exhaust gases from the cold start of the engine and running (idle engine speed) until the engine reached the optimal operating temperature. The test procedure involved saving the data collected by the analyzer every 10 s for each of the four tests performed (the duration of a test was 1050 s). The first test (No. 1) was performed without the use of the filtering system. Tests 2, 3, and 4 were carried out using the filtering system and changing the distance between the exhaust gases’ outlet point and the surface of the aqueous substance. All tests were carried out under similar conditions. Data specific to the test of engines were collected—emissions (CO2, CO, NOx), ambient temperature, and exhaust temperature. The tests were analyzed and compared, and the highest CO2 reductions without increases in CO or NOx were observed in Tests 3 and 4. Based on the detailed analysis of the values obtained from the four tests, the system was efficient. The tests will continue on experimental engines from test stands, to develop a prototype filter for primarily static applications with internal combustion engines: test stands for engines and generators, and, after homologation, directly on vehicles. The paper aims to partially solve an important problem—reducing the level of CO2 from the exhaust gases. The presented solution may have applicability in the automotive industry but is also feasible for static applications. Another objective is to reduce emissions from older vehicles, which are widespread in certain regions of Europe and worldwide.

Vehicles doi: 10.3390/vehicles8010018

Authors: Hao Wang Jundi Wang Hong Chen Changchao Li

This paper presents a kind of dual-channel coupled radial magnetic field resolver (DCCRMFR). The exciting winding and signal winding of this resolver adopt the structure of orthogonal phase. The number of turns and distribution of the four phase signal winding have been designed. The rotor has a double-wave magnetic conductive material structure. The variable reluctance mechanism between the stator and the rotor is derived by analytical method, and the feasibility of changing the coupling area for variable reluctance is obtained. The inductance of DCCRMFR was theoretically derived through the winding function method and combined with the finite element simulation method to obtain the inductance variation law and verify the correctness of the resolver design. Then simulation analysis was conducted on the output signal of DCCRMFR to extract the total harmonic distortion (THD) of the envelope of the electromotive force (EMF) output from the signal winding. Taking THD as the optimization objective, the optimized DCCRMFR simulation model is obtained by analyzing the air-gap length between the stator and the rotor and the thickness ratio of rotor. Finally, experimental measurements were conducted on a prototype model of a two pole pairs DCCRMFR, and the measurement results were compared and analyzed with simulation results to verify the correctness of the structural design and optimization of this DCCRMFR.

Vehicles doi: 10.3390/vehicles8010017

Authors: Xuekai Han Yingchun Qi Yuqiong Li Jiangquan Li Jianzhong Zhu Fa Su Heshu Huang Shiyi Zhu Meng Zou Lianbin He

Vehicle repeated passes over soft terrain alter the soil’s bearing and shear behavior, thereby affecting vehicle mobility and energy consumption. To address this issue, this study conducted cyclic compression and shear tests on beach sand with moisture contents of 5%, 15%, and 25%. A constitutive model incorporating the coupling effects of loading cycles (N) and moisture content (ω) was developed based on the Bekker and Janosi model framework. The model expresses compression parameters as functions of N and ω, and describes shear behavior through the strength evolution function k(N,ω) and deformation modulus function h(N,ω). Results show excellent agreement between the model predictions and experimental data (R2 > 0.92). Furthermore, a vehicle–soil coupled dynamics model was established based on the proposed constitutive model, forming a comprehensive analytical framework that integrates soil meso-mechanics with full vehicle–terrain interaction. This work provides valuable theoretical and technical support for predicting vehicle trafficability on coastal soft soils and optimizing vehicle suspension systems.

Vehicles doi: 10.3390/vehicles8010016

Authors: Chris Michael Hongjian Wang

Object detection for autonomous vehicles under adverse weather conditions—such as rain, fog, snow, and low light—remains a significant challenge due to severe visual distortions that degrade image quality and obscure critical features. This paper presents YOLOv11-TWCS, an enhanced object detection model that integrates TransWeather, the Convolutional Block Attention Module (CBAM), and Spatial-Channel Decoupled Downsampling (SCDown) to improve feature extraction and emphasize critical features in weather-degraded scenes while maintaining real-time performance. Our approach addresses the dual challenges of weather-induced feature degradation and computational efficiency by combining adaptive attention mechanisms with optimized network architecture. Evaluations on DAWN, KITTI, and Udacity datasets show improved accuracy over baseline YOLOv11 and competitive performance against other state-of-the-art methods, achieving mAP@0.5 of 59.1%, 81.9%, and 88.5%, respectively. The model reduces parameters and GFLOPs by approximately 19–21% while sustaining high inference speed (105 FPS), making it suitable for real-time autonomous driving in challenging weather conditions.

Vehicles doi: 10.3390/vehicles8010015

Authors: Changcheng Yin Yiyang Liu Jiwei Zhang Hui Yuan Baohua Wang Yunfei Zhang

Improving the ride comfort of commercial vehicles is crucial for driver health and operational safety. This study focuses on optimizing the parameters of a cab suspension system to improve its vibration isolation performance. Initially, nonlinear fitting was applied to experimental data characterizing air spring stiffness and damping, which informed the development of a multi-body rigid-flexible coupled dynamic model of the suspension system; its dynamic characteristics were subsequently validated through modal analysis. Road excitation data, filtered through the chassis suspension, were collected during vehicle testing, and displacement excitations for ride comfort simulation were reconstructed using virtual iteration technology. Thereafter, an integrated ISIGHT platform, combining ADAMS and MATLAB, was employed to systematically optimize suspension parameters and key bushing stiffness via a multi-island genetic algorithm. The optimization results demonstrated significant performance improvements: on General roads, the overall weighted root-mean-square acceleration was markedly reduced with enhanced isolation efficiency; on Belgian pave roads, resonance in the cab’s X-axis direction was effectively suppressed; and on Cobblestone roads, the pitch angle was successfully constrained within the design limit. This research provides an effective parameter matching methodology for performance optimization of cab suspension systems.

Vehicles doi: 10.3390/vehicles8010014

Authors: Oleksandr Osetrov Rainer Haas

Direct injection in hydrogen engines enables flexible combustion control, improves engine efficiency, and reduces the risk of abnormal combustion. However, implementing this injection strategy is challenging due to the need to provide a relatively high volumetric fuel flow rate, achieve a specified degree of mixture stratification, and account for the functional and technological limitations of the injection system. These challenges highlight the relevance and objectives of the present study. The mathematical model of a turbocharged engine cycle has been refined to account for the influence of injection parameters on combustion kinetics. On the basis of mathematical modeling, the injection pressure and injector area were determined to ensure the specified injection conditions. For the late injection strategy, a method was proposed to select the start of injection based on a specified value of the “relative ignition timing” criterion. Engine operation was simulated across the full range of operating modes for both early and late injection strategies. The results show that the late injection strategy increases the maximum indicated thermal efficiency by approximately 2%, reduces peak in-cylinder pressure by about 1 MPa, lowers maximum nitrogen oxide emissions by a factor of 1.4, and ensures knock-free operation across all modes compared to early injection.

Vehicles doi: 10.3390/vehicles8010012

Authors: Carl Hübner Günther Prokop

A device under test, when applied to the test rig, often does not come with much information about its mechanical properties to the user. There are different applications in which specific properties of the device under test are of interest to the user. Therefore, a suitable model approach and a parameterisation method are required. If there is a torsional model of the plant, including the device under test and the load machines, it can, for example, be used in a model predictive control architecture. The focus of the publication is on the frequency range of driveability (f< 30 Hz) and, in particular, on the phenomenon of the vehicle shuffle mode, which is important for driving comfort. The model approach has to map these characteristics. To make this possible, the publication presents a suitable, simplified modelling approach for electric powertrains in the hardware-in-the-loop environment and the possibility of indirect parameterisation for the moment of inertia and stiffness. The investigations demonstrate that the model possesses the essential eigenmodes and frequencies observed in the measurements on the test rig. Taking into account extensions, the model enables the incorporation of the properties of an open differential, including delta speeds. The natural frequency matches the measured one with deviations less than 1%. The results also show that the parameters are smaller than assumed. The authors will revise the developed method on this basis to achieve higher information value and a better confidence interval. This further work will discuss the influence of the confidence interval on the resulting parameters.

Vehicles doi: 10.3390/vehicles8010013

Authors: Weiwei Yang Yong Jiang Yijun Han Yilin Wang

To address the issue of insufficient trajectory tracking accuracy and the stability of distributed drive mining dump trucks under complex working conditions, this paper proposes a model predictive control (MPC) strategy based on genetic-particle swarm optimization (GAPSO). This strategy overcomes the limitations of traditional MPC controllers—where the weight matrix is fixed—by constructing a hierarchical optimization architecture that enables adaptive weight adjustment. An MPC-based trajectory tracking controller is developed using a three-degree-of-freedom vehicle dynamics model. Furthermore, to address the challenge of tuning MPC weight parameters, a GAPSO-based fusion optimization algorithm is introduced. This algorithm integrates the global search capability of genetic algorithms with the local convergence advantages of particle swarm optimization, enabling joint optimization of the state and control weight matrices. Simulation results demonstrate that under complex scenarios such as double lane change maneuvers, varying vehicle speeds, and different road adhesion coefficients, the proposed GAPSO-MPC controller significantly outperforms conventional MPC and PSO-MPC approaches in terms of lateral position tracking root mean square error. The method effectively enhances the robustness of trajectory tracking for distributed drive mining vehicles under disturbance conditions, offering a viable technical solution for high-precision control in autonomous mining systems.

Vehicles doi: 10.3390/vehicles8010011

Authors: Huanrui Zhang Xiaoyue Zhang Chunhua Cheng Xinyi Lv Chunxi Zhang

High-altitude long-endurance (HALE) UAVs require navigation payloads that are both fully autonomous and lightweight. This paper presents a full-parameter calibration method for a dual-axis rotational-modulation RINS/CNS integrated system in which the IMU is mounted on a two-axis indexing mechanism and the reconnaissance camera is reused as the star sensor. We establish a unified error propagation model that simultaneously covers IMU device errors (bias, scale, cross-axis/installation), gimbal non-orthogonality and encoder angle errors, and camera exterior/interior parameters (EOPs/IOPs), including Brown–Conrady distortion. Building on this model, we design an error-decoupled calibration path that exploits (i) odd/even symmetry under inner-axis scans, (ii) basis switching via outer-axis waypoints, and (iii) frequency tagging through rate-limited triangular motions. A piecewise-constant system (PWCS)/SVD analysis quantifies segment-wise observability and guides trajectory tuning. Simulation and hardware-in-the-loop results show that all parameter groups converge primarily within the segments that excite them; the final relative errors are typically ≤5% in simulation and 6–16% with real IMU/gimbal data and catalog-based star pixels.

Vehicles doi: 10.3390/vehicles8010010

Authors: Xiaoqing Qiao Li Xie Yun Yang Chao Luo

The rapid development of China’s high-speed railways (HSRs) and the implementation of revenue management policies have promoted the marketization of railway passenger transport, which is mainly reflected in the gradual transformation from a seller’s market dominated by operating companies to a buyer’s market dominated by passenger demand. Passenger travel needs are becoming increasingly diverse. In order to improve the quality of HSR services and attract more passengers, this paper starts from passenger satisfaction and considers the heterogeneity of travel preferences of passengers with different travel distances. Based on the passenger travel data of the Nanning-Guangzhou (NG) HSR line, the K-means clustering method is used to classify passengers into three categories: short-distance, medium-distance, and long-distance travel. A structural equation modeling–multinomial logit (SEM-MNL) model integrating both explicit and latent variables was constructed to analyze passenger travel origin-destination (OD) choices. Stata software was used to estimate passenger preferences for perceived satisfaction functions across different travel distances. Finally, considering constraints such as load factor, departure capacity, and spatiotemporal passenger flow demand, a line planning optimization model was constructed with the goal of minimizing train operating costs and maximizing passenger travel satisfaction. An improved subtraction optimizer algorithm was designed for the solution. Using the NG Line as a case study, the proposed method achieved a reduction in train operating costs while enhancing overall passenger satisfaction.