Vehicles

Complex mountainous environments pose significant challenges for dynamic path planning and obstacle avoidance of transport vehicles. In response, this paper presents an innovative path planning approach that combines an enhanced A* algorithm with the artificial potential field (APF) method. Firstly, the heuristic function of the A* algorithm was improved, and path inflection points were optimized to enhance global path-planning efficiency and smoothness. Secondly, a target distance factor was introduced to modify the APF algorithm’s repulsive field function, solving the traditional APF’s target-unreachable problem. The integrated algorithm uses the A*-optimized inflection points as sub-target points for the APF, meeting real-time obstacle avoidance requirements in dynamic environments and conducting secondary path planning to avoid local minima. Impressively, static environment simulations demonstrated the integrated algorithm’s outstanding path-planning capabilities in complex terrains. Moreover, dynamic obstacle avoidance experiments revealed its remarkable ability to not only detect and evade dynamic obstacles but also maintain a safe distance from static ones. The findings highlight that this method significantly boosts path-planning efficiency while ensuring safety and global optimality in dynamic settings. This breakthrough offers crucial theoretical support for enhancing the navigation of mountain transport vehicles in complex, real-world scenarios, potentially improving their operation. Full article

Extreme events such as tropical cyclones frequently occur in coastal areas in China. With high wind speeds and rainfall during such extreme events, the vehicles on sea-crossing bridges may face severe instability problems. In this study, the dynamics of vehicles on a cross-sea bridge under the wind–rain coupling effect were analyzed based on field measurement data using computational fluid dynamics (CFD). Wind field parameters of the coastal area in China were obtained using wind speed data from measurement towers. Based on CFD, the sliding grid method was applied to establish an aerodynamic analysis model of a container truck moving on a bridge under wind and rain conditions. The discrete phase model based on the Euler–Lagrange method was used to investigate the influence of rain and obtain the aerodynamic characteristics of the truck under the coupled wind and rain effects. Based on the computational analysis results, considering the turbulence intensity, the yaw angle peaks of the tractor and trailer increased by 5.2% and 3.8%, respectively, and the lateral displacement of the truck’s center of mass increased by 9.8%. Rainfall may cause the vehicle to have a higher response, resulting in a high risk of skidding. The results show that skidding occurs for the considered container truck when rainfall is at 9.8%. These results can provide parameters for traffic control strategies under such extreme climate events in coastal areas. Full article

Due to the complexity of the unstructured environment and the high-level requirement of smoothness when a tracked transportation vehicle is traveling, making the vehicle travel as safely and smoothly as when a skilled operator is maneuvering the vehicle is a critical issue worth studying. To this end, this study proposes a trajectory-planning method for human-like maneuvering. First, several field equipment operators are invited to manipulate the model vehicle for obstacle avoidance driving in an outdoor scene with densely distributed obstacles, and the manipulation data are collected. Then, in terms of the lateral displacement, by comparing the similarity between the data as well as the curvature change degree, the data with better smoothness are screened for processing, and a dataset of human manipulation behaviors is established for the training and testing of the trajectory-planning network. Then, using the dynamic parameters as constraints, a two-stage planning approach utilizes a modified deep network model to map trajectory points at multiple future time steps through the relationship between the spatial environment and the time series. Finally, after the experimental test and analysis with multiple methods, the root-mean-square-error and the mean-average-error indexes between the planned trajectory and the actual trajectory, as well as the trajectory-fitting situation, reveal that this study’s method is capable of planning long-step trajectory points in line with human manipulation habits, and the standard deviation of the angular acceleration and the curvature of the planned trajectory show that the trajectory planned using this study’s method has a satisfactory smoothness. Full article

Desert highways, with open terrain and minimal wind barriers, expose high-speed vehicles to significant stability risks from combined crosswinds and sand accumulation. This study uses numerical simulation to assess the effects of varying wind direction angles and sand thicknesses on vehicle stability across different models. Five dynamic indicators—lateral displacement, yaw angle, aerodynamic sideslip angle, lateral acceleration, and roll angle—are analyzed. The results show that a 120° wind angle causes the most pronounced parameter changes, while stability is lowest at 150°, where critical thresholds are reached within 0.75 s and danger thresholds by 2.25 s. Rapid wind speed variations further degrade stability. Compared to small SUVs, mid-size SUVs perform worse under identical conditions. A comprehensive stability evaluation function is proposed to quantify the combined impact of wind angle and surface friction, providing a new approach for safety assessment on sand-covered desert roads. Full article

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