Explore the vast range of titles available.

Drivers frequently adjust their path due to crosswinds and overtaking, where adjacent vehicles significantly alter airflow. This study uses computational fluid dynamics to analyze the aerodynamic impact of overtaking maneuvers on simplified car models (Ahmed Bodies) under crosswind conditions. The investigation focuses on how drag, lift, and side force coefficients change during different overtaking stages at varying crosswind angles (0°, 15°, 30°, and 45°). The study focused on 2 Ahmed Body models, which are overtaking vehicle(A) and overtaking vehicle(B), in 5 different cases: before overtake, initiation of overtake, mid-overtake, completion of overtake, and after overtake. Results show that at a 15° crosswind, Car A has a higher drag coefficient (Cd: 0.3916), reducing performance and stability. At 30°, Car A shows a high lift coefficient (Cl: 0.9881); at 45°, Car B experiences a significant increase in side force coefficient (Cs: 3.1192). This is due to the pressure contour at the front corner of the vehicle surface and the vortex formation on the leeward side of the vehicles as yaw angles rise. Results show that crosswinds significantly increase aerodynamic forces and alter flow structures around vehicles. Specifically, the relative position of vehicles during overtaking greatly influences these forces, affecting vehicle stability.

Traffic congestion or accidents may occur as a consequence of the difficulty of performing a safe, comfortable, and efficient overtaking in a timely manner when there is a slow or stopped vehicle, cyclist, or partial lane blockage on the road. Specifically, most drivers find it challenging to overtake a sluggish vehicle on a single-lane road in the presence of vehicles coming from other directions. To resolve such overtaking concerns, this paper proposes a novel optimal trajectory generating scheme for autonomous vehicle overtaking that is both smooth and safe and can be used in a variety of traffic scenarios. The proposed scheme is based on the solution of an optimal predictive problem with the goal of minimizing driving costs while limiting collision risks in the presence of any opposite vehicle on the overtaking lane. The computational burden of the scheme is almost negligible and can be implemented in real-time. The scheme is evaluated in a variety of traffic conditions, including stopped and slow vehicles in the lane, as well as the presence or absence of a nearby opposite vehicle. The simulation results show that the proposed scheme effectively obtains the optimal trajectories even in the difficult overtaking contexts considering various constraints imposed by the road curve, opposite vehicles, and slow preceding vehicles. Finally, the optimal overtaking costs are obtained for various states of the associated vehicles, which provide an indication of the best state to initiate the overtake. The proposed technology can be employed as a fully automated system or an advanced driver assistance system (ADAS) to improve the vehicle flows at challenging driving conditions and enhance transportation sustainability.

To address the bottlenecks of low sample efficiency and poor control accuracy in traditional single-layer reinforcement learning during autonomous driving overtaking, this paper proposes an overtaking decision and control system based on hierarchical reinforcement learning to decouple complex tasks in spatial and temporal dimensions. A heterogeneous two-layer architecture is constructed, where the upper layer adopts the Proximal Policy Optimization algorithm to generate macroscopic discrete decisions, while the lower layer employs Twin Delayed Deep Deterministic Policy Gradient combined with Long Short-Term Memory to achieve smooth continuous control of steering and acceleration by perceiving temporal features of dynamic obstacles. A composite reward mechanism, integrating hard safety constraints and soft efficiency incentives, is designed to balance safety, efficiency, and comfort. Experimental results in complex scenarios with multiple interfering vehicles and random lane-changing behaviors demonstrate that the proposed system improves the training convergence speed by approximately 30% within 500,000 steps compared to single-layer algorithms. In tests across varying traffic densities, the system achieves a 98.3% success rate in medium-density scenarios with a collision rate of only 0.6%. In high-density challenges, the success rate remains above 95%, with the collision rate reduced by about 80% compared to baseline models. Furthermore, the lateral control deviation is strictly limited to within 0.2 m, and the longitudinal safety distance remains stable above 5 m. This system provides a robust, high-efficiency paradigm for autonomous overtaking.

To investigate the influence of drivers’ cognitive differences on the vehicle driving process, this paper focuses on the mechanism of drivers’ cognition of vehicles, roads, and environments. Existing studies of vehicle following and overtaking usually set the driver reaction time, vehicle braking deceleration, and acceleration as fixed values, ignoring the individual differences of drivers and their influence by road conditions. Therefore, firstly, the concept of “trust level of driving” is proposed and real vehicle tests and simulator tests for different drivers and different working conditions are designed to quantify the trust level of driving. Addressing the limitations of existing car‐following and overtaking models, a safety distance model and a two‐way dual‐lane highway overtaking model are developed based on trust level of driving. Finally, the driving simulator is used to create scenarios, and the effectiveness of the model is verified. The safe vehicle distance and overtaking distance required by drivers with different trust levels of driving are obtained, and their applicability is explained. In this study, a more accurate and humanized model of safe distance and overtaking was developed to help address driver cognitive deficits and improve traffic safety.

The traffic environment of mountainous highways is more complex than that of nonmountainous highways, with higher driving loads, which increases the risk in overtaking. The changes in the driver’s pupils, eye gaze behavior, and heart rate can be used to evaluate the level of driving tension and safety. To analyze the driving load while overtaking on two‐lane highways in mountainous areas, an actual vehicle test was conducted. Twenty‐one drivers were divided into a skilled group and an unskilled group. The gaze time, gaze transfer characteristics, heart rate changes, and pupil area changes during the three stages of overtaking (intention, execution, and return) were compared and analyzed. The comprehensive evaluation of driving load during the overtaking process used the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method and Rank Sum Ratio (RSR) method. The results show that the two groups of drivers had the highest driving load during the overtaking execution stage and the lowest driving load during the intention stage. The driving load of overtaking on sections with poor‐sight distance was significantly higher than that on sections with good‐sight distance, and the risk in overtaking during the execution and return stages was highest on sections with poor‐sight distance. It is possible to reduce the driving load if the driver is familiar with the road conditions or has a rich driving experience. Compared to the unskilled group, the skilled group had lower driving loads at all stages of overtaking. The research results can provide a theoretical basis for optimizing traffic safety prevention and control technology on mountainous highways and for designing intelligent driving assistance.

This paper present three distinct probability-based methods for decision making and trajectory planning layers of overtaking maneuvering functionality for autonomous vehicles. The computation time of the proposed decision-making algorithms may be high, because the number of describing parameters of the traffic situations may vary in a high range. The presented clustering-based, graph-based and dynamic-based methods differ in the complexity of their computation algorithms. Since the decision-making process may require considerable online computation effort, a neural-network-based approach is presented for implementation purposes.

One major concern in the development of intelligent vehicles is to improve the driving safety. It is also an essential issue for future autonomous driving and intelligent transportation. In this paper, we present a vision-based system for driving assistance. A front and a rear on-board camera are adopted for visual sensing and environment perception. The purpose is to avoid potential traffic accidents due to forward collision and vehicle overtaking, and assist the drivers or self-driving cars to perform safe lane change operations. The proposed techniques consist of lane change detection, forward collision warning, and overtaking vehicle identification. A new cumulative density function (CDF)-based symmetry verification method is proposed for the detection of front vehicles. The motion cue obtained from optical flow is used for overtaking detection. It is further combined with a convolutional neural network to remove repetitive patterns for more accurate overtaking vehicle identification. Our approach is able to adapt to a variety of highway and urban scenarios under different illumination conditions. The experiments and performance evaluation carried out on real scene images have demonstrated the effectiveness of the proposed techniques.

Overtaking is a crucial maneuver in road transportation that requires a clear view of the road ahead. However, limited visibility of ahead vehicles can often make it challenging for drivers to assess the safety of overtaking maneuvers, leading to accidents and fatalities. In this paper, we consider atrous convolution, a powerful tool for explicitly adjusting the field-of-view of a filter as well as controlling the resolution of feature responses generated by Deep Convolutional Neural Networks in the context of semantic image segmentation. This article explores the potential of seeing-through vehicles as a solution to enhance overtaking safety. See-through vehicles leverage advanced technologies such as cameras, sensors, and displays to provide drivers with a real-time view of the vehicle ahead, including the areas hidden from their direct line of sight. To address the problems of safe passing and occlusion by huge vehicles, we designed a see-through vehicle system in this study, we employed a windshield display in the back car together with cameras in both cars. The server within the back car was used to segment the car, and the segmented portion of the car displayed the video from the front car. Our see-through system improves the driver’s field of vision and helps him change lanes, cross a large car that is blocking their view, and safely overtake other vehicles. Our network was trained and tested on the Cityscape dataset using semantic segmentation. This transparent technique will instruct the driver on the concealed traffic situation that the front vehicle has obscured. For our findings, we have achieved 97.1% F1-score. The article also discusses the challenges and opportunities of implementing see-through vehicles in real-world scenarios, including technical, regulatory, and user acceptance factors.

With the rapid development of the economy, urban road congestion has become more serious. The travel times for vehicles are becoming more uncontrollable, making it challenging to reach destinations on time. In order to find an optimal route and arrive at the destination with the shortest travel time, this paper proposes a dynamic shortest travel time path planning algorithm with an overtaking function (DSTTPP-OF) based on a vehicular ad hoc network (VANET) environment. Considering the uncertainty of driving vehicles, the target vehicle (vehicle for special tasks) is influenced by surrounding vehicles, leading to possible deadlock or congestion situations that extend travel time. Therefore, overtaking planning should be conducted through V2V communication, enabling surrounding vehicles to coordinate with the target vehicle to avoid deadlock and congestion through lane changing and overtaking. In the proposed DSTTPP-OF, vehicles may queue up at intersections, so we take into account the impact of traffic signals. We classify road segments into congested and non-congested sections, calculating travel times for each section separately. Subsequently, in front of each intersection, the improved Dijkstra algorithm is employed to find the shortest travel time path to the destination, and the overtaking function is used to prevent the target vehicle from entering a deadlocked state. The real-time traffic data essential for dynamic path planning were collected through a VANET of symmetrically deployed roadside units (RSUs) along the roadway. Finally, simulations were conducted using the SUMO simulator. Under different traffic flows, the proposed DSTTPP-OF demonstrates good performance; the target vehicle can travel smoothly without significant interruptions and experiences the fewest stops, thanks to the proposed algorithm. Compared to the shortest distance path planning (SDPP) algorithm, the travel time is reduced by approximately 36.9%, and the waiting time is reduced by about 83.2%. Compared to the dynamic minimum time path planning (DMTPP) algorithm, the travel time is reduced by around 18.2%, and the waiting time is reduced by approximately 65.6%.

In mixed pedestrian–vehicle traffic environments, overtaking pedestrians by vehicles is a prevalent and complex human–vehicle interaction scenario. However, this maneuver often leads to accidents, resulting in injuries and fatalities, primarily due to inadequate in frastructure, limited pedestrian safety awareness, and suboptimal driver behavior. To mitigate such accidents and develop active vehicle safety systems and autonomous driving algorithms based on human–vehicle interaction data, it is crucial to investigate the overtaking behavior of human drivers. This study examines driver overtaking behavior under various conditions through driving simulator experiments and evaluates how different experimental variables influence driver performance. Using data from 12 skilled drivers, a risk corridor for vehicles overtaking pedestrians is established and a lateral distance prediction model is developed. Based on this established risk corridor, a vehicle overtaking strategy is proposed. Furthermore, to assess the risk level associated with overtaking pedestrians, pedestrians’ subjective risk perceptions are quantified. The simulation results indicate that the maximum lateral error of the vehicle is approximately 0.14 m, the maximum heading error is about 0.06 radians, and the vehicle’s trajectory during pedestrian overtaking remains within the defined risk corridor. These findings are consistent with the operational characteristics of human drivers.