Vehicles, Volume 7, Issue 2 (June 2025) – 34 articles

This paper introduces a novel neuromorphic-inspired hyper-heuristic framework (NeuHH) for solving the Capacitated Single-Allocation p-Hub Location Routing Problem (CSAp-HLRP), a challenging combinatorial optimization problem that jointly addresses hub location decisions, capacity constraints, and vehicle routing. The proposed framework employs Spiking Neural Networks (SNNs) as the decision-making core, leveraging their temporal dynamics and spike-timing-dependent plasticity (STDP) to guide the real-time selection and adaptation of low-level heuristics. Unlike conventional learning-based hyper-heuristics, NeuHH provides biologically plausible, event-driven learning with improved scalability and interpretability. Experimental results on benchmark instances demonstrate that NeuHH outperforms classical metaheuristics, Lagrangian relaxation methods, and reinforcement learning-based hyper-heuristics. Specifically, NeuHH achieves superior performance in total cost minimization (up to 13.6% reduction), load balance improvement (achieving a load balance factor of as low as 1.04), and heuristic adaptability (reflected by higher heuristic switching frequency). These results highlight the framework’s potential for real-time and energy-efficient logistics optimization in large-scale dynamic networks. Full article

Computer vision is one of the key technologies of advanced driver assistance systems (ADAS), but the incorporation of a vision-based driver assistance system (still) poses a great challenge due to the special characteristics of the algorithms, the neural network architecture, the constraints, and the strict hardware/software requirements that need to be met. The aim of this study is to show the influence of image resolution in traffic lane detection using a virtual dataset from virtual simulation environment (CARLA) combined with a real dataset (TuSimple), considering four performance parameters: Mean Intersection over Union (mIoU), F1 precision score, Inference time, and processed frames per second (FPS). By using a convolutional neural network (U-Net) specifically designed for image segmentation tasks, the impact of different input image resolutions (512 × 256, 640 × 320, and 1024 × 512) on the efficiency of traffic line detection and on computational efficiency was analyzed and presented. Results indicate that a resolution of 512 × 256 yields the best trade-off, offering high mIoU and F1 scores while maintaining real-time processing speeds on a standard CPU. A key contribution of this work is the demonstration that combining synthetic and real datasets enhances model performance, especially when real data is limited. The novelty of this study lies in its dual analysis of simulation-based data and image resolution as key factors in training effective lane detection systems. These findings support the use of synthetic environments in training neural networks for autonomous driving applications. Full article

This study investigates the accuracy of traffic flow and speed measurements using two radar-based devices, Sierzega and SDR, against manual video-based traffic counts. The measurements were conducted over a 12-h period on a congested urban road section characterized by variable traffic conditions and frequent vehicle stops. The results revealed that the SDR device generally provided lower deviations compared to manual counting, especially in measuring traffic flow. In contrast, the Sierzega device demonstrated greater and more inconsistent deviations, particularly in vehicle categorization and traffic density estimation. The observed discrepancies were primarily attributed to vehicle stopping and queuing, influencing length estimation and classification errors. Despite these limitations, SDR provided sufficient accuracy for practical applications, such as monitoring traffic trends or supporting long-term traffic planning in urban environments. Full article

The evaluation of electric buses used in public transportation operations encompasses several critical factors that directly influence the operational efficiency, as well as the economic viability, environmental advantages, and user experience. Energy consumption is a critical metric for assessing the energy efficiency of electric buses. It facilitates a better understanding of vehicle performance across varying road conditions and advances the implementation of energy-saving solutions. The passenger demand model is a tool used to assess the quality and experience of electric buses, with the assessment being based on real usage. The operational mileage is defined as the driving distance of electric buses on a single charge. This parameter has a significant impact on both urban coverage and route optimization. The article under consideration identifies evaluation indicators for electric buses. These indicators are derived from a set of 100 questionnaire responses, which were collected in Győr, Hungary. The classification of the indicators into three segments—mechanical, operational and bus transportation system—is proposed, with the underlying rationale and significance of each indicator’s selection being elucidated. The findings indicate that this component is essential for developing a comprehensive evaluation system for electric buses and serves as a solid foundation for more intricate future studies. Full article

The World Health Organization reports approximately 1.35 million fatalities annually due to road traffic accidents, with pedestrians constituting 23% of these deaths. This highlights the critical need to enhance pedestrian safety, especially given the significant role human error plays in road accidents. Autonomous vehicles present a promising solution to mitigate these fatalities by improving road safety through advanced prediction of pedestrian behavior. With the autonomous vehicle market projected to grow substantially and offer various economic benefits, including reduced driving costs and enhanced safety, understanding and predicting pedestrian actions and intentions is essential for integrating autonomous vehicles into traffic systems effectively. Despite significant advancements, replicating human social understanding in autonomous vehicles remains challenging, particularly in predicting the complex and unpredictable behavior of vulnerable road users like pedestrians. Moreover, the inherent uncertainty in pedestrian behavior adds another layer of complexity, requiring robust methods to quantify and manage this uncertainty effectively. This review provides a structured and in-depth analysis of pedestrian intention prediction techniques, with a unique focus on how uncertainty is modeled and managed. We categorize existing approaches based on prediction duration, feature type, and model architecture, and critically examine benchmark datasets and performance metrics. Furthermore, we explore the implications of uncertainty types—epistemic and aleatoric—and discuss their integration into autonomous vehicle systems. By synthesizing recent developments and highlighting the limitations of current methodologies, this paper aims to advance the understanding of Pedestrian intention Prediction and contribute to safer and more reliable autonomous vehicle deployment. Full article

Reduced gravity may impair motion perception accuracy, especially in the absence of visual cues, which could degrade astronauts’ driving performance. The lack of prior research makes simulating realistic motion perception for lunar rover driving particularly challenging. We created a simulation system to quantitatively simulate the motion characteristics of a lunar rover at different gravity levels, and a software program based on the spatial orientation observer model was developed for the comparison of motion perception differences between Earth’s and lunar gravity. In comparison to Earth’s gravity, the lunar rover in lunar gravity demonstrates the following differences: (1) The rover exhibits a greater propensity to float and slip, and slower acceleration and deceleration. (2) Dynamic tilt perception may be more complicated with single vestibular information, while static tilt perception is greatly reduced; the introduction of visual information can notably improve the perception accuracy. Simulation results demonstrate that motion characteristics and perception of lunar rover driving exhibit a more variable trend at different gravity levels. An intuitive mathematical formulation was proposed to explain the single vestibular results. Our findings provide a basis for further optimizing lunar rover driving motion simulation strategies. Full article

This work systematically explores the impact of spark plug electrode number on engine performance and environmental effects, including noise, vibration, fuel consumption, and exhaust emissions. Indicators of combustion efficiency and mechanical health are engine vibration and noise; emissions directly affect ecological sustainability. Four-electrode spark plugs reduce vibration by 10%, noise by 5%, and fuel economy by 15%, according to experimental results showing they outperform single-electrode designs. Especially four-electrode designs also lower harmful hydrocarbon (HC) and carbon monoxide (CO) emissions by up to 20%, indicating more complete combustion and providing significant environmental benefits through lower air pollution and greenhouse gas emissions. Reduced exhaust temperatures of surface discharge plugs indicate better combustion efficiency and perhaps help with decarbonization. With poorer emission profiles, two- and three-electrode configurations raise fuel consumption, noise, and vibration. Reduced quenching effects, improved spark distribution, and accelerated flame propagation all help to explain enhanced combustion efficiency in multi-electrode designs and so affect the fundamental combustion chemistry. These results highlight the possibilities of four-electrode spark plugs to improve engine performance and reduce environmental impact, providing information for automotive engineers and legislators aiming at strict emissions standards (e.g., Euro 7) and sustainability targets. With an eye toward the chemical processes involved, additional study is required to investigate electrode geometry, material innovations, and lifetime environmental impacts. Full article

The increasing global demand for conventional energy has led to significant challenges, particularly due to rising CO₂ emissions and the depletion of natural resources. In the U.S., light-duty vehicles contribute significantly to transportation sector emissions, prompting a global shift toward electrified vehicles (EVs). Among the challenges that thwart the widespread adoption of EVs is the insufficient charging infrastructure (CI). This study focuses on exploring the complex relationship between EV adoption and CI growth. Employing a graph theoretic approach, we propose a graph model to analyze correlations between EV adoption and CI growth across 137 counties in six states. We examine how different time granularities impact these correlations in two distinct scenarios: Early Adoption and Late Adoption. Further, we conduct causality tests to assess the directional relationship between EV adoption and CI growth in both scenarios. Our main findings reveal that analysis using lower levels of time granularity result in more homogeneous clusters, with notable differences between clusters in EV adoption and those in CI growth. Additionally, we identify causal relationships between EV adoption and CI growth in 137 counties and show that causality is observed more frequently in Early Adoption scenarios than in Late Adoption ones. However, the causal effects in Early Adoption are slower than those in Late Adoption. Full article

Robust object detection in autonomous driving is challenged by inherent limitations of conventional frame-based cameras, such as motion blur and limited dynamic range. In contrast, event-based cameras, which operate asynchronously and capture rapid changes with high temporal resolution and expansive dynamic range, offer a promising augmentation. While the previous research on event-based object detection has predominantly focused on algorithmic enhancements via advanced preprocessing and network optimizations to improve detection accuracy, the practical engineering and integration challenges of deploying these sensors in real-world systems remain underexplored. To address this gap, our study investigates the integration of event-based cameras as a complementary sensor modality in autonomous driving. We adapted a conventional frame-based detection model (YOLOv8) for event-based inputs by training it on the GEN1 dataset, achieving a mean average precision (mAP) of 70.1%, a significant improvement over previous benchmarks. Additionally, we developed a real-time object detection pipeline optimized for event-based data, integrating it into the CARLA simulation environment and ROS for system prototyping. The model was further refined using transfer learning to better adapt to simulation conditions, and the complete pipeline was validated across diverse simulated scenarios to address practical challenges. These results underscore the feasibility of incorporating event cameras into existing perception systems, paving the way for their broader deployment in autonomous vehicle applications. Full article

The paper offers an overview of the motor vehicle brake pad wear process. Considering the types of wear that occur between the pads and the disc, the study begins by presenting Archard’s fundamental wear law. It explains how the hardness and roughness of materials can influence the wear rate. Furthermore, the analysis describes factors influencing the wear coefficient, including chemical affinity between materials, surface quality, thermo-elastic instability (TEI) of the materials, and environmental effects. The paper also presents detection systems for brake pad wear, such as sensors-based monitoring and artificial neural networks (ANNs). These systems monitor brake pad wear in real time, thereby improving the driving safety by alerting the driver to the condition of the brake pads. The principles and systems analyzed form the basis for predictive maintenance, minimizing the risks of brake failure due to excessive wear. Full article

Air pollution poses a serious threat to human health and the environment. Emissions from motor vehicles, especially in large cities, contribute significantly to this problem. This study analyzes the results of emission inspections in the Slovak Republic to identify factors influencing emissions and their impact on air quality. The research analyzed data from emission inspections and their relationship to vehicle age, fuel type, and type of failure. The results show that older vehicles, especially those aged 10 to 20 years, have a higher probability of failing to meet emission standards. Specifically, up to 42.75% of diesel vehicles aged 15 to 20 years were rated as unfit, compared to 33.07% of gasoline vehicles in the same age category. An increased proportion of unfit vehicles was recorded for diesel engines, which indicates their negative impact on air quality. The most common failures were related to direct emission measurements. These findings have implications for environmental policy and the regulation of vehicle imports to improve air quality and reduce pollution. Data on emission inspections were drawn from the national system and show knowledge about the observation of emission inspections carried out during one calendar year. The study recommends the introduction of stricter control mechanisms for older vehicles, supporting the renewal of the vehicle fleet, and the implementation of modern technologies to reduce emissions. Rigorous emission inspections are essential for the protection of public health. Regular inspections and modern technologies reduce emissions of harmful substances, thus contributing to the improvement of air quality and public health. Full article

Currently, one of the most important challenges facing autonomous vehicles’ development due to varying driving conditions is effective path tracking while considering lateral stability. To address this issue, this study proposes the optimization of the linear quadratic regulator (LQR) control system by using the genetic algorithm (GA) to support the vehicle in following the predefined path accurately, minimizing the sideslip, and stabilizing the vehicle’s yaw rate. The dynamic system model of the vehicle is represented based on yaw rate angle, lateral speed, and vehicle sideslip angle as the variables of the state space model, with the steering angle as an input parameter. Using the GA to optimize the LQR control by tuning the weighting of the Q and R matrices led to enhancing the system response and minimizing deviation errors via a proposed cost function of GA. The simulation results were obtained using MATLAB/Simulink 2024a, with a representation of a predefined path as a Gaussian path. Under external and internal disturbances, such as road conditions, lateral wind, and actuator delay, the model demonstrates improved tracking performance and reduced sideslip angle and lateral acceleration by adjusting the longitudinal vehicle speed. This work highlights the effectiveness of robust control in addressing path planning, driving stability, and safety in autonomous vehicle systems. Full article

Physics-assisted machine learning is a powerful framework that enhances data efficiency by integrating the strengths of conventional machine learning with physical knowledge. This paper applies this concept and focuses on the design of a driver evaluator using physics-assisted unsupervised learning, which serves as a virtual reference generator that provides different driving modes for vehicles equipped with active actuators. A strategy that applies sensitivity analysis regarding the vehicle handling performance, aiming to reduce the computational workload of the clustering algorithms, is proposed. First, a bicycle model with nonlinear Pacejka’s tire models is established for the analysis of lateral dynamics. Next, mathematical interpretations of sensitivity analysis are derived to evaluate the contribution of physical parameters to the system response and build the reduced parameters set. Then, Gaussian mixture models are fitted to a database generated with the full parameters set and another with the reduced set, respectively. Finally, step-steer and constant radius tests are performed to assess the handling performance with respect to the two validated centroids. Comparisons of lateral dynamics and understeer characteristics indicate that the proposed method can accurately distinguish driving modes in a much faster manner compared to traditional machine learning. This methodology has significant potential for practical applications with large databases and more complex systems. Full article

The current technical condition of facilities designated for the technical–hygienic maintenance of railway rolling stock is unsatisfactory, as they are neither technologically nor technically equipped to meet the required quality standards. Maintenance is often carried out in open spaces or directly on the tracks of major railway junctions, which prevents year-round execution of these services and causes operational limitations. This article analyses and proposes solutions for the technical–hygienic maintenance center (THU) of railway rolling stock at the Nové Zámky railway station in Slovakia, focusing on improving the efficiency and quality of the provided services. The analysis includes an assessment of technological procedures, identification of operational deficiencies, and a comparison of current maintenance standards with the requirements for contemporary railway systems, such as automated diagnostic platforms, predictive maintenance modules, and modular cleaning infrastructure. The optimization of THU services considers the average time norms for selected technological procedures and the characteristics of train sets passing through the center. The proposed solution involves a more efficient scheduling of operations in line with the valid railway traffic timetable and train set circulation, utilizing a graphical planning method for modelling and optimizing the facility’s service processes. The implementation of optimization measures can lead to increased capacity and efficiency of maintenance, reduced time required for individual procedures, and lower operational costs. The study’s results provide practical recommendations for improving the quality of technical–hygienic maintenance at railway junction stations, contributing to greater railway transport reliability and an overall improvement in passenger comfort. Additionally, the findings offer a transferable framework that may inform the planning and modernization of maintenance facilities at other regional railway stations facing similar infrastructural and operational challenges. Full article

This study investigates the connection between coherent structures in the flow around a vehicle and the aerodynamic forces acting on its body. Dynamic Mode Decomposition (DMD) was applied to analyze the flow field of a squareback Ahmed body at $R{e}_H=7.7\times {10}^5$. DMD enabled the identification of coherent structures in the near and far wake by isolating their individual oscillation frequencies and spatial energy distributions. These structures were classified into three regimes based on their underlying mechanisms: symmetry breaking, bubble pumping, and large-scale vortex shedding in range of $St\le 0.2$. The energy contributions of these flow regimes were quantified across different regions of the flow field and compared to the aerodynamic forces on the body. Additionally, the linear correlation between pressure and velocity components was examined using Pearson correlation coefficients of DMD spectral amplitudes. The locations of maximum and minimum correlation values, as well as their relationship to energy contributions, were identified and analyzed in detail. Full article

Vehicle congestion and the environmental problems associated with the increasing vehicle fleet have led stakeholders to create solutions to these problems. Park-and-Ride (P&R) facilities are provided as a solution for public transportation to avoid increasing vehicular flow and using private vehicles. However, the optimal location of these facilities is still a challenge to be considered. Therefore, this article aims to present a systematic review of the mathematical models applied for P&R localization, using the PRISMA protocol to ensure a comprehensive analysis. A total of 44 articles between 2002 and 2025 were identified into four categories: decision support models, econometric models, optimization models, and other models. The review also examines the term distribution of urban contexts where the mathematical models are applied, distinguishing between Global North versus Global South urban contexts. The results showed the efficiency of mathematical models within the decision support models category due to their integration with multiple criteria. The econometric models analyze factors influencing user behavior, while the optimization models improve and optimize the efficiency of transport networks despite facing computational challenges. Finally, other models, such as multilevel programming and fuzzy logic, offer adaptive solutions for highly variable urban environments. The primary contribution of this study is its comprehensive application of the mathematical models used for the location of P&R facilities. This offers a systematic approach for anticipating future urban situations, developing supporting policies, and analyzing their effects. Full article

The rapid evolution of autonomous vehicles (AVs) has ignited widespread interest in their potential to transform mobility and transportation ecosystems. However, despite significant technological advances, the acceptance of AVs by the public remains a complex and multifaceted challenge. This state-of-the-art review explores the key factors influencing AV acceptance, focusing on the intersection of artificial intelligence (AI) services, user experience, social dynamics, and regulatory landscapes across diverse global regions. By analyzing trust, perceived safety (PS), cybersecurity, and user interface design, this paper delves into the psychological and behavioral drivers that shape public perception of AVs. It also highlights the role of demographic segmentation and media influence in accelerating or hindering adoption. A comparative analysis of AV acceptance across North America, Europe, Asia, and emerging markets reveals significant regional variations, influenced by regulatory frameworks, economic conditions, and social trends. Also, this review reveals critical insights into the perceived safety associated with AV technology, including legal uncertainties and cybersecurity concerns, while emphasizing the future potential of AVs in urban environments, public transit, and autonomous logistics fleets. This review concludes by proposing strategic roadmaps and policy implications to accelerate AV adoption, offering a forward-looking perspective on how advances in technology, coupled with targeted industry and government initiatives, can shape the future of autonomous mobility. Through a comprehensive examination of current trends and challenges, this paper provides a foundation for future research and innovation aimed at enhancing public acceptance and trust in AVs. Full article

Pooled ridesharing offers on-demand, one-way, cost-effective transportation for passengers traveling in similar directions via a shared vehicle ride with others they do not know. Despite its potential benefits, the adoption of pooled rideshare remains low in the United States. This exploratory study aims to evaluate potential service improvements and features that may increase users’ willingness to adopt the service. The study analyzed transportation behaviors, rideshare preferences, and willingness to adopt pooled rideshare services among 8296 U.S. participants in 2025, building on findings from a 2021 nationwide survey of 5385 U.S. participants. The study incorporated 77 actionable items developed from the results of the 2021 survey to assess whether addressing specific user-generated topics such as safety, reliability, convenience, and privacy can improve pooled rideshare use. A side-by-side comparison of the 2021 and 2025 data revealed shifts in transportation behavior, with personal rideshare usage increasing from 22% to 28%, public transportation from 21% to 27%, and pooled rideshare from 6% to 8%, while personal vehicle (79%) use remained dominant. Participants rated features such as driver verification (94%), vehicle information (93%), peak time reliability (93%), and saving time and money (92–93%) as most important for improving rideshare services. A pre-to-post analysis of willingness to use pooled rideshare utilizing the actionable items as per respondents’ preferences showed improvement: “definitely will” increased from 15.9% to 20.1% and “probably will” rose from 35.6% to 47.7%. These results suggest that well-targeted service improvements may meaningfully enhance pooled rideshare acceptance. This study offers practical guidance for Transportation Network Companies (TNCs) and policymakers aiming to improve pooled rideshare as well as potential future research opportunities. Full article

Urban transportation systems evolve toward greater diversification, scalability, and complexity. To address the escalating issue of urban traffic congestion, leveraging modern information technologies to enhance the integration of multiple transportation modes and maximize overall efficiency has emerged as a promising strategy. This study focuses on the decision making problem of urban multimodal transportation travel paths, integrating the time-varying characteristics of public transportation schedules and networks. We consider passengers’ diverse needs and systematically investigate how to optimize travel paths to minimize travel time while adhering to constraints, such as the number of interchanges and travel costs. To address this NP-hard problem, we propose and implement two optimization algorithms: a variable-length coding genetic algorithm (V-GA) and a full permutation coding genetic algorithm (F-GA). Detailed numerical analysis validates the effectiveness of both algorithms, with the V-GA demonstrating significant advantages over the F-GA in terms of solution efficiency. Our findings provide novel perspectives and methodologies for optimizing urban multimodal transportation travel paths, offering robust theoretical foundations and practical tools for enhancing urban traffic planning and travel service efficiency. Full article

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