Vehicles, Volume 7, Issue 2 (June 2025) – 15 articles

Vehicle accidents, particularly PV-PV collisions, result in significant property damage and driver injuries, causing substantial economic losses and health risks. Most existing studies focus on macro-level predictions, such as accident frequency, but lack detailed collision-level analysis, which limits the precision of severity prediction. This study investigates various accident-related factors, including environmental conditions, vehicle attributes, driver characteristics, pre-crash scenarios, and collision dynamics. Data from NHTSA’s CRSS and FARS datasets were integrated and balanced using random over-sampling and under-sampling techniques to address severity-level data imbalances. The mRMR algorithm was employed for feature selection to minimize redundancy and identify key features. Five advanced machine learning models were evaluated for severity prediction, with XGBoost achieving the best performance: 84.9% accuracy, 84.85% precision, 84.90% recall, and an F1-score of 84.87%. SHAP analysis was utilized to interpret the model and conduct a comprehensive analysis of accident features, including their importance, dependencies, and combined effects on severity prediction. This study achieved high accuracy in predicting accident severity across all levels in PV-PV collisions. Moreover, by integrating the SHAP model interpretation method, we conducted detailed feature analysis at global, local, and individual case levels, thereby filling the gap in PV-PV accident severity prediction and feature analysis. Full article

Hybrid vehicles utilize multiple power sources, making them energy-efficient and enhancing both fuel efficiency and dynamic performance. As a result, hybrid vehicles have recently been adopted as race cars, which demand high powertrain performance. The hybrid vehicle system comprises two power sources: an internal combustion engine (ICE) and an electric motor, both of which require precise control. Controlling the output of the internal combustion engine is particularly challenging. This study investigated the dynamic response of an actuator in an electronic throttle system. The experimental results demonstrated that optimized parameters significantly improved the dynamic response. As a result, we propose a mechanism for hybrid vehicle performance and report the characteristics of an electronic throttle. The improvement in throttle opening can be verified by adjusting the P term. Full article

Safe vehicles are an important pillar in reducing the number of accidents or mitigating the consequences of a collision. Although the number of autonomous safety systems in vehicles is increasing, retrofitted systems could also help reduce road accidents. A new retrofit assistance system called Front Brake Light (FBL) helps the driver to assess the intentions of other road users. This system is mounted at the front of the vehicle and works similarly to the rear brake lights. The objective of this study is to evaluate the safety performance of an FBL in real accidents at junctions. Depending on the type of accident, between 7.5% and 17.0% of the accidents analysed can be prevented. A further 9.0% to 25.5% could be positively influenced by the FBL; i.e., the collision speed could be reduced. If the FBLs were visible to the driver of the priority vehicle, the number of potentially avoidable accidents would increase to a magnitude of 11.5% to 26.2%. The range of accidents in which the consequences can be reduced increases to between 13.8% and 39.2%. Full article

The transportation sector mainly relied on fossil fuel and is one of the major causes of climate change and environmental pollution. Advances in smart sensing technology are paving the way for the development of clean and intelligent vehicles that lead to a more sustainable transportation system. In response, the automotive industry is actively engaging in new sensor technologies and innovative control and diagnostic algorithms that improve energy sustainability and reduce vehicle emissions. In particular, recent regulations for diesel vehicles require the integration of smart soot sensors to deal with particulate filter on-board diagnostic (OBD) challenges. Meeting the recent, more stringent OBD requirements will be difficult using traditional diagnostic approaches. This study investigates an advanced diagnostic strategy to assess particulate filter health based on resistive soot sensors and available engine variables. The sensor data are projected to generate a 2D signature that reflects the changes in filtration efficiency. A relevant feature (character) is then extracted from the generated signature that can be transformed into an analytical expression used as an indicator of DPF malfunction. The diagnostic strategy uses an adaptive approach that dynamically adjusts the signature’s characters according to the engine’s operating conditions. A correction factor is calculated using an optimization algorithm based on the integral of engine speed measurements and IMEP set points during each sensor loading period. Different cost functions have been tested and evaluated to improve the diagnostic performance. The proposed adaptive approach is model-free and eliminates the need for subsystem models, iterative algorithms, and extensive calibration procedures. Furthermore, the time-consuming and inaccurate estimation of soot emissions upstream of the DPF is avoided. It was evaluated on a validated numerical platform under NEDC driving conditions with simultaneous dispersions on engine-out soot concentration and soot sensor measurements. The promising results highlight the robustness and superior performance of this approach compared to a diagnostic strategy solely reliant on sensor data. Full article

The latest developments in Advanced Driver Assistance Systems (ADAS) have greatly enhanced the comfort and safety of drivers. These technologies can identify driver abnormalities like fatigue, inattention, and impairment, which are essential for averting collisions. One of the important aspects of this technology is automated traffic accident detection and prediction, which may help in saving precious human lives. This study aims to explore critical features related to traffic accident detection and prevention. A public US traffic accident dataset was used for the aforementioned task, where various machine learning (ML) models were applied to predict traffic accidents. These ML models included Random Forest, AdaBoost, KNN, and SVM. The models were compared for their accuracies, where Random Forest was found to be the best-performing model, providing the most accurate and reliable classification of accident-related data. Owing to the black box nature of ML models, this best-fit ML model was executed with explainable AI (XAI) methods such as LIME and permutation importance to understand its decision-making for the given classification task. The unique aspect of this study is the introduction of explainable artificial intelligence which enables us to have human-interpretable awareness of how ML models operate. It provides information about the inner workings of the model and directs the improvement of feature engineering for traffic accident detection, which is more accurate and dependable. The analysis identified critical features, including sources, descriptions of weather conditions, time of day (weather timestamp, start time, end time), distance, crossing, and traffic signals, as significant predictors of the probability of an accident occurring. Future ADAS technology development is anticipated to be greatly impacted by the study’s conclusions. A model can be adjusted for different driving scenarios by identifying the most important features and comprehending their dynamics to make sure that ADAS systems are precise, reliable, and suitable for real-world circumstances. Full article

Modern powertrain configurations for electric vehicles introduce the possibility to actuate the wheel directly by means of in-wheel motors. These machines enable stiffer and more efficient traction, with the possibility of introducing pitch motion control due to the intrinsic coupling between longitudinal, vertical, and pitch dynamics. This paper proposes a pitch rate attenuation control exploiting a Lyapunov function that attempts to cancel the pitch rate dynamics from the model. Unlike previous works, this pitch control is performed exclusively with the traction machine; it does not rely on controllable suspension systems. The controller formulation guarantees global stability of the vehicle. Furthermore, it considers the nonlinearity of the plant introduced by the dependency on the pitch angle. To facilitate the feedback of the road profile needed by the Lyapunov controller, two Kalman filters are included in the control law. This work implements the described strategy on a half car model. Simulations examine different speed and road conditions. It is demonstrated that the control strategy can blend longitudinal and pitch rate attenuation torque commands using a rear in-wheel motor, attaining a reduction of up to $41\%$ for chassis pitch rate and $36\%$ for pitch acceleration. Full article

This study investigates the impact of a conductive Electric Road System (ERS) rail on vehicle dynamics and stability through numerical simulations. The ERS rail, designed for dynamic charging of electric vehicles, was modeled and tested under various operational conditions, including different vehicle types (SUV and city car) and skid resistance levels (Side-friction coefficient (SFC) ranging from 0.20 to 0.60). Simulations were implemented at multiple speeds (50 to 130 km/h) to assess longitudinal, lateral, vertical accelerations, roll, yaw, pitch angles, and braking performance during lane changes and emergency braking maneuvers. Experimental tests using instrumented vehicles (Peugeot E-2008, Renault Clio 3) were conducted to calibrate the numerical model and validate the simulation results. Key findings reveal that, while the ERS rail slightly increases vertical acceleration and braking distance, it does not compromise overall vehicle stability. Lane-change tests showed minimal trajectory deviations (below 0.20 m) and acceleration levels remained within safety limits. However, discomfort was noted at higher speeds (90–110 km/h) with low skid resistance (SFC = 0.20). This comprehensive evaluation provides valuable insights into the safety and operational performance of ERS rails, emphasizing the importance of optimizing rail skid resistance to ensure practical large-scale deployment and enhanced road safety. Full article

This study investigates the task allocation problem for multiple mobile robots in complex real-world scenarios. To address this challenge, a distributed game-theoretic approach is proposed to enable collaborative decision-making. First, the task allocation problem for multiple mobile robots is formulated to optimize the resource utilization. The formulation also takes into account comprehensive constraints related to robot positioning and task timing. Second, a game model is established for the proposed problem, which is proved to be an exact potential game. Furthermore, we introduce a novel utility function for the tasks to maximize the resource utilization. Based on this formulation, we develop a game-theoretic coalition formation algorithm to seek the Nash equilibrium. Finally, the algorithm is evaluated via simulation experiments. Another six algorithms are used for comparative studies. When the problem scale is small, the proposed algorithm can achieve solution quality comparable to that of the benchmark algorithms. In contrast, under larger and more complex problem instances, the proposed algorithm can achieve up to a 50% performance improvement over the benchmarks. This further confirms the effectiveness and superiority of the proposed method. In addition, we evaluate the solution quality and response time of the algorithm, as well as its sensitivity to initial conditions. Finally, the proposed algorithm is applied to a post-disaster rescue scenario, where the task allocation results further demonstrate its superior performance. Full article

The deployment of autonomous vehicles (AVs) is gaining popularity due to their predicted safety and operational benefits and is driven by advancements in the automobile industry. However, due to the unavailability of fully AVs in the automobile market, users’ perception of their adoption is driven by available knowledge and personal attitudes towards AVs. The effects of users’ perception of policy requirements, the potential effects of AVs, and their familiarity with AV technology on their willingness to ride AVs are investigated in this research. The effect of personal characteristics, such as gender and education level, on users’ perceptions of various aspects related to AVs is also modeled. Stated preference survey data of 2323 respondents from the United States was used for modeling, and three models were developed using confirmatory factor analysis and structural equation modeling (SEM) techniques. The results show that users’ perception of the required policies restricting AVs, the influence of widespread AVs, and their familiarity with AV technology are unrelated. Persons with higher education levels and females were found to give more weight to policies restricting AVs than the potential effects of AVs. Users’ familiarity with AV technology and their perception of the anticipated effects of AVs were found to positively influence their willingness to ride AVs. Even though users favored policies restricting the use of AVs in certain areas, they were still willing to ride them. The findings provide valuable insights for policymakers to restrict the use of AVs in certain areas during their early deployment stages. They can also assist automobile manufacturers in prioritizing and focusing on technical advancements that will increase their acceptance and penetration into the market. Full article

The testing and pilot operations of autonomous vehicles are currently booming in terms of real-world operations. Although the validation and verification methods are not standardized, nor is the legislation, as well as the methodology of data collection on autonomous vehicles’ performance and safety. The safety of autonomous vehicles can be inferred from the collision and disengagement reports provided by manufacturers and operators. This report documents instances when a human driver or operator took control of an autonomous vehicle during testing in detail. Disengagement reports are primarily aimed at safety and performance evaluation of autonomous vehicles, but can they be the basis for determining the readiness of autonomous driving technology and technological progress? This study analyzes disengagement reports to assess their utility in determining autonomous vehicles’ progress and readiness. Our findings indicate a declining trend in reported disengagements, despite increased operational distances, suggesting possible improvements in autonomous vehicle technology. However, disparities in data collection, varying operational design domains, and inconsistent reporting practices among manufacturers limit direct comparability. These factors challenge the reliability of disengagement reports as a definitive measure of technological evolution. The study highlights the need for more standardized and transparent reporting to better assess autonomous vehicle safety and development trends. Full article

The automotive sector plays an essential role in the Romanian economy, making a significant contribution to industrial production and employment. This study conducts a comprehensive bibliometric analysis of scholarly publishing in the Romanian automotive sector. By analyzing publication trends, citation patterns, and collaboration networks, the study maps the evolution of research in this field and highlights key contributions and future directions. The findings reveal a significant increase in research output over the past two decades, with a focus on emerging fields such as artificial intelligence, electric and autonomous vehicles, and sustainable mobility solutions. The analysis also identifies leading researchers and institutions and explores collaboration networks between Romanian and international actors. These insights provide valuable benchmarks for assessing Romania’s position in the global automotive research arena and inform strategies for future research efforts. Full article

Electrification and modularity are emerging as key trends in off-road vehicle development, prompting the need for innovative solutions in steering and modular coupling. This study presents an electromechanical connection and steering joint, conceived to replace traditional hydraulic systems and offer enhanced steering precision, modular adaptability, and system efficiency. By eliminating hydraulic components, the design reduces fluid leakage risks, lowers maintenance requirements, and improves energy integration with the vehicle’s electric drivetrain. The joint enables independent module articulation, including steering and controlled tilting, to optimize vehicle stability across diverse terrains. A prototype was built and tested under real-world conditions, assessing functional reliability, ease of integration, and operational performance. The findings demonstrate that electromechanical steering substantially boosts system flexibility compared to conventional hydraulic setups. Full article

Tires are important for the transmission of forces, good traction of the vehicle, and safety of the passengers. Tires also influence vehicle fuel consumption and cause tire and road wear pollution to the environment in the form of microplastics. In the United States, the Uniform Tire Quality Grading (UTQG) for tread wear is reported on the tire sidewall and is used as an indicator of the expected service life of a tire. In Europe, a similar approach that applies tread depth reduction measurements and projection to the minimum tread depth is under discussion. Tread depth measurements will be carried out in parallel with abrasion measurements over the recently introduced abrasion rate test in the United Nations regulation 117. Testing is carried out with an on-road convoy method accompanied by a vehicle fitted with reference tires to minimize the influence of external parameters. In this brief review, we start with a short historical overview of the methods that have been applied so far for the measurement of tire service life. Based on the limited publicly available data, we calculate the average tread depth reduction per distance driven for summer and winter tires fitted both in the front and rear axles of passenger cars (1–1.2 mm for front wheels and 0.5–0.6 mm for rear wheels per 10,000 km). We theoretically estimate the tread mass loss per mm of tread depth reduction (250 g per 1 mm tread depth reduction, depending on the tire size) and we compare the values to experimental data obtained in recent campaigns. We give estimations of the tire service life as a function of the tread wear UTQG (100 times the indicated tread wear rating). We also discuss the projected service life using tread depth reduction and mass loss. Full article

The collision avoidance capability of autonomous vehicles in extreme traffic conditions remains a focal point of research. This paper introduces an Adaptive Cruise Control (ACC) strategy based on Model Predictive Control (MPC) and Responsibility-Sensitive Safety (RSS) models. Simulations were conducted in the CARLA environment, where the lead vehicle underwent various rapid deceleration scenarios to optimize the following vehicle’s braking strategy. By integrating the multi-step predictive optimization capabilities of MPC with the dynamic safety assessment mechanisms of RSS, the proposed strategy ensures safe following distances while achieving rapid and precise speed adjustments, thereby enhancing the system’s responsiveness and safety. The model also incorporates a secondary optimization to balance comfort and stability, thereby improving the overall performance of autonomous vehicles. The use of multi-dimensional assessment metrics, such as Time to Collision (TTC), Time Exposed TTC (TET), and Time Integrated TTC (TIT), addresses the limitations of using TTC alone, which only reflects instantaneous collision risk. The optimization of the model in this paper aims to improve the safety and comfort of the following vehicle in scenarios with various gap distances, and it has been validated through the SSM multi-indicator approach. Experimental results demonstrate that the improved ACC model significantly enhances vehicle safety and comfort in scenarios involving large gaps and short-distance emergency braking by the lead vehicle, validating the method’s effectiveness in various extreme traffic scenarios. Full article