Vehicles

The presented research is focused on the development of the bus rapid transit (BRT) system, combining the high capacity of rail transport with the flexibility of bus routes. Classic BRT systems have certain limitations, particularly concerning a single rolling stock capacity. The main motivation of the work is to find efficient and cost-effective solutions to increase passenger traffic in the BRT system while optimizing fuel consumption. The main contribution of this study is the comprehensive analysis and optimization of various configurations of trailer bus trains, which represent a flexible and cost-effective alternative to traditional single or articulated buses. Based on two schemes, four possible options for using trailer bus trains are offered, which differ in the number of sections and working engines. Among the suggested schemes of trailer bus trains, the two-section and three-section schemes with all engines running and the three-section scheme with one engine turned off are appropriate for use due to improved fuel efficiency indicators with better or acceptable traction and speed properties. Calculations carried out on a mathematical model show that, for example, a two-section bus train can provide a reduction of specific fuel consumption per passenger by 6.3% compared to a single bus at full load, while a three-section train can provide even greater savings of up to 8.4%. Selective shutdown of one of the engines in a multi-section train can lead to an additional improvement in fuel efficiency by 5–10%, without leading to a critical reduction in the required traction characteristics. Full article

Passenger safety remains a primary goal in vehicle engineering, requiring the development of advanced passive safety systems to reduce injuries during collisions. Impact attenuators (particularly for race cars) are a crucial component for the safety of the driver. The impact of the impact attenuator (IA) is demonstrated by the behavior of a seat-belted dummy in a frontal collision with a rigid wall. The aim of this paper is to confirm the qualities of water as a damping agent in the manufacturing of the IA. To reach a conclusion, a theoretical model is used and experimental tests are performed. Once the loads operating on the dummy have been identified, it is confirmed that they fall within the range that the existing requirements recommend. The car is viewed as a structure with a seat-belt-fastened dummy and an impact attenuator. Research is being conducted on a new water-based impact attenuator technology. A frontal collision of the car–dummy assembly was taken into consideration when analyzing the dummy’s behavior in accordance with the criteria. A simulation program was used to calculate the accelerations at various points on the mannequin’s body as well as the force that manifested on the seat belts. So, the good qualities of IAs using water are revealed and support designers in their efforts to obtain better shock behavior. In the simulation, the variation of internal energy accumulated by the vehicle, displacements and velocities of various points on the chassis, as well as the accelerations of the vehicle and the occupant were determined. In the experiment, the vehicle velocities for both test cases were established and used in the simulation, and the accelerations of the vehicle and dummy were measured. The assessment was carried out by comparing experimental and simulation data, focusing on acceleration values recorded on both the dummy and the vehicle. Evaluation criteria such as HIC and ThAC were applied to determine the severity of the impact and the effectiveness of the proposed water-based attenuator. Full article

Connected and Autonomous Vehicles (CAVs) remain vulnerable to cyberattacks due to inherent security gaps in the Controller Area Network (CAN) protocol. We present a structured Python (3.11.13) framework that repairs structural inconsistencies in a public CAV dataset to improve the reliability of machine learning-based intrusion detection. We assess the effect of training data volume and compare Random Forest (RF) and Extreme Gradient Boosting (XGBoost) classifiers across four attack types: DoS, Fuzzy, RPM spoofing, and GEAR spoofing. XGBoost outperforms RF, achieving 99.2 % accuracy on the DoS dataset and 100 % accuracy on the Fuzzy, RPM, and GEAR datasets. The Synthetic Minority Oversampling Technique (SMOTE) further enhances minority-class detection without compromising overall performance. This methodology provides a generalizable framework for anomaly detection in other connected systems, including smart grids, autonomous defense platforms, and industrial control networks. Full article

Traffic congestion has increased significantly in today’s rapidly urbanizing world, influencing people’s daily lives. Traffic signal control systems (TSCSs) play an important role in alleviating congestion by optimizing traffic light timings and improving road efficiency. Yet traditional TSCSs neglected pedestrians, cyclists, and other non-monitored road users, degrading traffic signal optimization (TSO). Therefore, this framework proposes a multi-object-based traffic flow analysis and intensity estimation model for efficient TSO using Upper Confidence Bound Multi-agent Reinforcement Learning Cubic Spline Fuzzy Logic (UCB-MRL-CSFL). Initially, the real-time traffic videos undergo frame conversion and redundant frame removal, followed by preprocessing. Then, the lanes are detected; further, the objects are detected using Temporal Context You Only Look Once (TC-YOLO). Now, the object counting in each lane is carried out using the Cumulative Vehicle Motion Kalman Filter (CVMKF), followed by queue detection using Vehicle Density Mapping (VDM). Next, the traffic flow is analyzed by Feature Variant Optical Flow (FVOF), followed by traffic intensity estimation. Now, based on the siren flashlight colors, emergency vehicles are separated. Lastly, UCB-MRL-CSFL optimizes the Traffic Signals (TSs) based on the separated emergency vehicle, pedestrian information, and traffic intensity. Therefore, the proposed framework outperforms the other conventional methodologies for TSO by considering pedestrians, cyclists, and so on, with higher computational efficiency (94.45%). Full article

Active suspension can improve vehicle vibrations caused by road excitation. For trucks, the vehicle mass change is usually large, and changes in vehicle mass will affect the control performance of the active suspension. In order to solve the problem of active suspension control performance decreasing due to large changes in vehicle mass, this paper proposes an active suspension control method integrating online mass estimation. This control method is designed based on the mass estimation algorithm of the recursive least squares method with a forgetting factor (FFRLS) and the Linear Quadratic Regulator (LQR) algorithm. A set of feedback control matrices $K$ is obtained according to different vehicle masses. Then, the mass estimation algorithm can estimate the actual vehicle mass in real-time during the vehicle acceleration process. According to the mass estimation value, a corresponding feedback control matrix $K$ is selected from the control matrix set, and $K$ is used as the actual control gain matrix of the current active suspension. With specific simulation cases, the vehicle vibration response is studied by the numerical simulation method. The results of the simulation process have shown that when the vehicle mass changes largely, the suspension dynamic deflection and tire dynamic deformation are significantly reduced while keeping a good vehicle body attitude control effect by using an active suspension controller integrated with online mass estimation. In the random road simulation, suspension dynamic deflection is reduced by 3.26%, and tire dynamic deformation is reduced by 5.91% compared with the original active suspension controller. In the road bump simulation, suspension dynamic deflection and tire dynamic deformation are also significantly reduced. As a consequence, the stability and comfort of the vehicle have been greatly enhanced. Full article

Differential planetary roller lead screw (DPRS) serves as a quintessential actuating mechanism within the electromechanical braking (EMB) systems of vehicles, where its operational reliability is paramount to ensuring braking safety. Considering different braking intensities, how assembly errors affect the contact stress in DPRS was analyzed via the finite element method. Firstly, the braking force of the EMB system that employed DPRS was verified by the braking performance of legal provisions. Secondly, a rigid body dynamics model of DPRS was established to analyze the response time, braking clamping force, and axial contact force of DPRS under varied braking intensities. Finally, a finite element model of DPRS was constructed. The impact of assembly errors in the lead screw and rollers on the contact stress were investigated within the DPRS mechanism based on this model. The results indicate that as braking intensity increases, the deviation of the lead screw exerts a greater influence on the contact stress generated by the engagement between the lead screw and rollers compared to that between the nut and rollers. The skewness of the rollers also affects the contact stress generated by the engagement of both the lead screw with rollers and the nut with rollers. When assembly errors reach a certain threshold, the equivalent plastic strain is induced to exceed the critical value. This situation significantly impairing the normal operation of DPRS. This study provides guidance for setting the threshold of assembly errors in DPRS mechanisms. It also holds significant implications for the operational reliability of EMB systems. Full article

Aiming at the problems of complex control processes, strong model dependence, and difficult engineering application when the existing active suspension control strategy is applied to multi-axle vehicles, an active suspension control strategy based on the inverse position solution of a parallel mechanism is proposed. First, the active suspension of the three-axle emergency rescue vehicle is grouped and interconnected within the group, and it is equivalently constructed into a 3-DOF parallel mechanism. Then, the displacement of each equivalent suspension actuating hydraulic cylinder is calculated by using the method of the inverse position solution of a parallel mechanism, and then the equivalent actuating hydraulic cylinder is reversely driven according to the displacement, thereby realizing the effective control of the attitude of the vehicle body. To verify the effectiveness of the proposed control strategy, a three-axis vehicle experimental platform integrating active suspension and hydro-pneumatic suspension was built, and a pulse road experiment and gravel pavement experiment were carried out and compared with hydro-pneumatic suspension. Both types of road experimental results show that compared to hydro-pneumatic suspension, the active suspension control strategy based on the inverse position solution of a parallel mechanism proposed in this paper exhibits different degrees of advantages in reducing the peak values of the vehicle vertical displacement, pitch angle, and roll angle changes, as well as suppressing various vibration accelerations, significantly improving the vehicle’s driving smoothness and handling stability. Full article

The integration of connected autonomous vehicles (CAVs), advanced driver assistance systems (ADAS), and conventional vehicles necessitates the development of robust methodologies to enhance traffic efficiency and ensure safety across heterogeneous traffic streams. A comprehensive understanding of vehicle interactions and operating speed variability is essential to support informed decision-making in traffic management and infrastructure design. This study presents operating speed models aimed at estimating the 85th percentile speed (V₈₅) on straight road segments, utilizing floating car data (FCD) for both calibration and validation purposes. The dataset encompasses approximately 2000 km of the Italian road network, characterized by diverse geometric features. Speed observations were analyzed under three traffic conditions: general traffic, free-flow, and free-flow with dry pavement. Results indicate that free-flow conditions improve the model’s explanatory power, while dry pavement conditions introduce greater speed variability. Initial models based exclusively on geometric parameters exhibited limited predictive accuracy. However, the inclusion of posted speed limits significantly enhanced model performance. The most influential predictors identified were the V₈₅ on the preceding curve and the length of the straight segment. These findings provide empirical evidence to inform road safety evaluations and geometric design practices, offering insights into driver behavior in mixed-traffic environments. The proposed model supports the development of data-driven strategies for the seamless integration of automated and non-automated vehicles. Full article

This study investigates the knowledge, perception and trust in Advanced Driver Assistance Systems (ADAS) among drivers in Eastern Sicily, a Mediterranean region characterized by infrastructural and socio-economic differences. A structured survey (N = 961) was conducted to assess user attitudes towards eight key ADAS technologies using two validated indices: the Knowledge Index (KI) and the Importance Index (II). To capture user consistency, a normalized product (z(KI) × z(II)) was calculated for each technology. This composite metric enabled the identification of three latent dimensions through exploratory factor analysis: Emergency-Triggered Systems, Adaptive and Reactive Systems and Driver Vigilance and Stability Systems. The results show a clear discrepancy between perceived importance (56.6%) and actual knowledge (35.1%). Multivariate analyses show that direct experience with ADAS-equipped vehicles significantly increases both awareness and confidence. Age is inversely correlated with knowledge, while gender has only a marginal influence. The results are consistent with established acceptance models such as TAM and UTAUT, which emphasize the role of perceived usefulness and trust. The study presents an innovative integration of psychometric metrics and behavioral theory that provides a robust and scalable framework for assessing user readiness in evolving mobility contexts, particularly in regions facing infrastructural heterogeneity and cultural changes in travel behavior. Full article

Evaluating noise, vibration, and harshness (NVH) performance is crucial in vehicle development. However, NVH evaluation is often subjective and challenging to achieve through numerical simulation, and typically prototypes are required. Dynamic driving simulators are emerging as a viable solution for assessing NVH performance in the early development phase before physical prototypes are available. However, most current simulators can reproduce vibrations only in a single direction or within a limited frequency range. This paper presents a comprehensive design optimization approach to enhance the dynamic response of a full-spectrum driving simulator, addressing these limitations. Specifically, in complex driving simulators, vibration crosstalk is a critical and common issue, which usually leads to an inaccurate dynamic response of the system, compromising the realism of the driving experience. Vibration crosstalk manifests as undesired vibration components in directions other than the main excitation direction due to structural coupling. To limit the system crosstalk, a flexible multibody dynamics model of the driving simulator has been developed, validated, and employed for a global sensitivity analysis. From this analysis, it turns out that the bushings located below the seat play a crucial role in the crosstalk characteristics of the system and can be effectively optimized to obtain the desired performances. Bushings’ stiffness and locations have been used as design variables in a multiobjective optimization with the aims of increasing the direct transmissibility of the actuators’ excitation and, at the same time, reducing the crosstalk contributions. A surrogate model approach is employed for reducing the computational cost of the process. The results show substantial crosstalk reduction, up to 57%. The proposed method can be effectively applied to improve the dynamic response of driving simulators allowing for their extensive use in the assessment of vehicles’ NVH performances. Full article

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