Search Results (35)

Autonomous vehicles are increasingly becoming a part of our daily lives, with active chassis control systems playing a pivotal role and drawing significant attention from both academia and industry. Current research on vehicle-to-vehicle overtaking behavior predominantly focuses on low-to-moderate speeds, with insufficient studies addressing high-speed lane-changing maneuvers. Under high-speed conditions, the variability and complexity of road environments significantly increase tracking errors, posing challenges for control algorithms that perform well at lower speeds but may suffer from reduced accuracy or instability at higher speeds. A hybrid control strategy based on vehicle dynamics for high-speed overtaking path tracking is developed to ensure vehicle stability and maneuverability. By integrating Model Predictive Control (MPC) with Four-Wheel Steering (4WS) controllers and employing a two-degree-of-freedom ideal model as the path-tracking response model, we have achieved effective control and path tracking for autonomous vehicles equipped with four-wheel steering. The effectiveness of the proposed control strategy was validated on the Carsim–Simulink integrated simulation platform. Experimental results demonstrate that this strategy offers higher path-tracking accuracy than single-controller approaches under high-speed conditions while also meeting vehicle stability requirements. The model provides robust support for enhancing the path-tracking performance of autonomous four-wheel steering vehicles at medium-to-high speeds, thereby advancing the reliability and safety of autonomous driving technology in practical applications. Full article

In recent years, unmanned surface vehicles (USVs) have gained increasing attention in industry due to their efficiency and versatility in marine operations. Artificial potential field (APF) methods, with their strong adaptability and simplicity of implementation, are widely used in USV path planning tasks. However, the naive APF method struggles in static complex environments, due to the local minima problem. Not to mention that actual navigations may involve other dynamic traffic participants. In this work, an improved APF algorithm integrating the boundary potential field method and the International Regulations for Preventing Collisions at Sea (COLREGs) is proposed. By incorporating the boundary potential field method, this novel approach effectively reduces the computational burden caused by clusters of land obstacles in complex environments, significantly improving computational efficiency. Furthermore, the APF method is refined to ensure the algorithm strictly adheres to COLREGs in head-on, overtaking, and crossing encounters, generating smooth and safe collision avoidance paths. The proposed method was tested in numerous complex scenarios derived from electronic navigational charts. The simulation results demonstrated the robustness and efficiency of the proposed algorithm for collision avoidance within complex maritime environments, providing reliable technical support for autonomous obstacle avoidance in dynamic ocean conditions. Full article

Despite the increasing sophistication of autonomous vehicles (AVs) and promises of increased safety, accidents will occur. These will corrode public trust and negatively impact user acceptance, adoption and continued use. It is imperative to explore methods that can potentially reduce this impact. The aim of the current paper is to investigate the efficacy of informational assistants (IAs) varying by anthropomorphism (humanoid robot vs. no robot) and dialogue style (conversational vs. informational) on trust in and blame on a highly autonomous vehicle in the event of an accident. The accident scenario involved a pedestrian violating the Highway Code by stepping out in front of a parked bus and the AV not being able to stop in time during an overtake manoeuvre. The humanoid (Nao) robot IA did not improve trust (across three measures) or reduce blame on the AV in Experiment 1, although communicated intentions and actions were perceived by some as being assertive and risky. Reducing assertiveness in Experiment 2 resulted in higher trust (on one measure) in the robot condition, especially with the conversational dialogue style. However, there were again no effects on blame. In Experiment 3, participants had multiple experiences of the AV negotiating parked buses without negative outcomes. Trust significantly increased across each event, although it plummeted following the accident with no differences due to anthropomorphism or dialogue style. The perceived capabilities of the AV and IA before the critical accident event may have had a counterintuitive effect. Overall, evidence was found for a few benefits and many pitfalls of anthropomorphising an AV with a humanoid robot IA in the event of an accident situation. Full article

In the context of increasing research on algorithms for different modules of the autonomous driving stack, the development and evaluation of these algorithms for deployment onboard vehicles is the next critical step. In the development and verification phases, simulations play a pivotal role in achieving this aim. The uncertainty quantification of Autonomous Vehicle (AV) systems could be used to enhance safety assurance and define the error-handling capabilities of autonomous driving systems (ADSs). In this paper, a virtual validation methodology for the control module of an autonomous driving stack is proposed. The methodology is applied to a rule-defined Model Predictive Controller (MPC)-based motion planner, where uncertainty quantification (UQ) is performed across various scenarios, based on the intended functionality within the algorithm’s operational design domain (ODD). The framework is designed to assess the performance of the algorithm under localization uncertainties, while performing obstacle vehicle-overtaking, vehicle-following, and safe-stopping maneuvers. Full article

Autonomous vehicles operating in public transportation spaces must rapidly and accurately detect all potential hazards in their surroundings to execute appropriate actions such as yielding, lane changing, and overtaking. This capability is a prerequisite for achieving advanced autonomous driving. In autonomous driving scenarios, distant objects are often small, which increases the risk of detection failures. To address this challenge, the MST-YOLOv8 model, which incorporates the C2f-MLCA structure and the ST-P2Neck structure to enhance the model’s ability to detect small objects, is proposed. This paper introduces mixed local channel attention (MLCA) into the C2f structure, enabling the model to pay more attention to the region of small objects. A P2 detection layer is added to the neck part of the YOLOv8 model, and scale sequence feature fusion (SSFF) and triple feature encoding (TFE) modules are introduced to assist the model in better localizing small objects. Compared with the original YOLOv8 model, MST-YOLOv8 demonstrates a 3.43% improvement in precision (P), an 8.15% improvement in recall (R), an 8.42% increase in mAP_0.5, a reduction in missed detection rate by 18.47%, a 70.97% improvement in small object detection AP, and a 68.92% improvement in AR. Full article

This paper introduces an enhanced APF method to address challenges in automatic lane changing and collision avoidance for autonomous vehicles, targeting issues of infeasible target points, local optimization, inadequate safety margins, and instability when using DLQR. By integrating a distance adjustment factor, this research aims to rectify traditional APF limitations. A safety distance model and a sub-target virtual potential field are established to facilitate collision-free path generation for autonomous vehicles. A path tracking system is designed, combining feed-forward control with DLQR. Linearization and discretization of the vehicle’s dynamic state space model, with constraint variables set to minimize control-command costs, aligns with DLQR objectives. The aim is precise steering angle determination for path tracking, negating lateral errors due to external disturbances. A Simulink–CarSim co-simulation platform is utilized for obstacle and speed scenarios, validating the autonomous vehicle’s dynamic hazard avoidance, lane changing, and overtaking capabilities. The refined APF method enhances path safety, smoothness, and stability. Experimental data across three speeds reveal reasonable steering angle and lateral deflection angle variations. The controller ensures stable reference path tracking at 40, 50, and 60 km/h around various obstacles, verifying the controller’s effectiveness and driving stability. Comparative analysis of visual trajectories pre-optimization and post-optimization highlights improvements. Vehicle roll and sideslip angle peaks, roll-angle fluctuation, and front/rear wheel steering vertical support forces are compared with traditional LQR, validating the optimized controller’s enhancement of vehicle performance. Simulation results using MATLAB/Simulink and CarSim demonstrate that the optimized controller reduces steering angles by 5 to 10°, decreases sideslip angles by 3 to 5°, and increases vertical support forces from 1000 to 1450 N, showcasing our algorithm’s superior obstacle avoidance and lane-changing capabilities under dynamic conditions. Full article

Automated driving (AD) is a new technology that aims to mitigate traffic accidents and enhance driving efficiency. This study presents a deep reinforcement learning (DRL) method for autonomous vehicles that can safely and efficiently handle highway overtaking scenarios. The first step is to create a highway traffic environment where the agent can be guided safely through surrounding vehicles. A hierarchical control framework is then provided to manage high-level driving decisions and low-level control commands, such as speed and acceleration. Next, a special DRL-based method called deep deterministic policy gradient (DDPG) is used to derive decision strategies for use on the highway. The performance of the DDPG algorithm is compared with that of the DQN and PPO algorithms, and the results are evaluated. The simulation results show that the DDPG algorithm can effectively and safely handle highway traffic tasks. Full article

The overtaking process for autonomous vehicles must prioritize both efficiency and safety, with safe distance being a crucial parameter. To address this, we propose an automatic overtaking path planning method based on minimal safe distance, ensuring both maneuvering efficiency and safety. This method combines the steady movement and comfort of the constant velocity offset model with the smoothness of the sine function model, creating a mixed-function model that is effective for planning lateral motion. For precise longitudinal motion planning, the overtaking process is divided into five stages, with each stage’s velocity and travel time calculated. To enhance the control system, the model predictive control (MPC) algorithm is applied, establishing a robust trajectory tracking control system for overtaking. Numerical simulation results demonstrate that the proposed overtaking path planning method can generate smooth and continuous paths. Under the MPC framework, the autonomous vehicle efficiently and safely performs automatic overtaking maneuvers, showcasing the method’s potential to improve the performance and reliability of autonomous driving systems. Full article

Automated driving is a promising development in reducing driving accidents and improving the efficiency of driving. This study focuses on developing a decision-making strategy for autonomous vehicles, specifically addressing maneuvers such as lane change, double lane change, and lane keeping on highways, using deep reinforcement learning (DRL). To achieve this, a highway driving environment in the commercial multi-body simulation software IPG Carmaker 11 version is established, wherein the ego vehicle navigates through surrounding vehicles safely and efficiently. A hierarchical control framework is introduced to manage these vehicles, with upper-level control handling driving decisions. The DDPG (deep deterministic policy gradient) algorithm, a specific DRL method, is employed to formulate the highway decision-making strategy, simulated in MATLAB software. Also, the computational procedures of both DDPG and deep Q-network algorithms are outlined and compared. A set of simulation tests is carried out to evaluate the effectiveness of the suggested decision-making policy. The research underscores the advantages of the proposed framework concerning its convergence rate and control performance. The results demonstrate that the DDPG-based overtaking strategy enables efficient and safe completion of highway driving tasks. Full article

Understanding the spatiotemporal characteristics of merging behavior is crucial for the advancement of autonomous driving technology. This study aims to analyze on-ramp vehicle merging patterns, and investigate how various factors, such as merging scenarios and vehicle types, influence driving behavior. Initially, a framework based on a high-definition (HD) map is developed to extract trajectory information in a meticulous manner. Subsequently, eight distinct merging patterns are identified, with a thorough examination of their behavioral characteristics from both temporal and spatial perspectives. Merging behaviors are examined temporally, encompassing the sequence of events from approaching the on-ramp to completing the merge. This study specifically analyzes the target lane’s spatial characteristics, evaluates the merging distance (ratio), investigates merging speed distributions, compares merging patterns and identifies high-risk situations. Utilizing the latest aerial dataset, exiD, which provides HD map data, the study presents novel findings. Specifically, it uncovers patterns where the following vehicle in the target lane chooses to accelerate and overtake rather than cutting in front of the merging vehicle, resulting in Time-to-Collision (TTC) values of less than 2.5 s, indicating a significantly higher risk. Moreover, the study finds that differences in merging speed, distance, and duration can be disregarded in patterns where vehicles are present both ahead and behind, or solely ahead, suggesting these patterns could be integrated for simulation to streamline analysis and model development. Additionally, the practice of truck platooning has a significant impact on vehicle merging behavior. Overall, this study enhances the understanding of merging behavior, facilitating autonomous vehicles’ ability to comprehend and adapt to merging scenarios. Furthermore, this research is significant in improving driving safety, optimizing traffic management, and enabling the effective integration of autonomous driving systems with human drivers. Full article

Overtaking on a dual-lane road with the presence of oncoming vehicles poses a considerable challenge in the field of autonomous driving. With the assistance of high-definition maps, autonomous vehicles can plan a relatively safe trajectory for executing overtaking maneuvers. However, the creation of high-definition maps requires extensive preparation, and in rural areas where dual two-lane roads are common, there is little pre-mapping to provide high-definition maps. This paper proposes an end-to-end model called OG-Net (Overtaking Guide Net), which accomplishes overtaking tasks without map generation or communication with other vehicles. OG-Net initially evaluates the likelihood of a successful overtaking maneuver before executing the necessary actions. It incorporates the derived probability value with a set of simple parameters and utilizes a Gaussian differential controller to determine the subsequent vehicle movements. The Gaussian differential controller effectively adapts a fixed geometric curve to various driving scenarios. Unlike conventional autonomous driving models, this approach employs uncomplicated parameters rather than RNN-series networks to integrate contextual information for overtaking guidance. Furthermore, this research curated a new end-to-end overtaking dataset, CarlaLanePass, comprising first-view image sequences, overtaking success rates, and real-time vehicle status during the overtaking process. Extensive experiments conducted on diverse road scenes using the Carla platform support the validity of our model in achieving successful overtaking maneuvers. Full article

An overall framework of the virtual testing system has been established based on the analysis of the virtual testing requirements for autonomous navigation performance of unmanned surface vehicles (USVs). This system consists of several modules, including the environment module, motion module, sensor module, and 3D visualization module. Firstly, within the robot operating system (ROS) environment, a three-dimensional navigation environment was generated by combining actual wave spectra with Gerstner waves. By designing a power plugin for USV navigation, the system was made to reflects the coupled motion model of USVs in wind, waves and currents, along with predictive results. Regarding the four typical sensor information on USVs, the actual sensors were virtualized, and a simulation approach for virtual sensor information is provided. The three-dimensional visualization of USV’s motion enables the intuitive display and analysis of the virtual testing process. Based on the prediction of coupled motion characteristics in wind, waves and currents, the interaction between USVs and the virtual testing system has been realized. A platform for virtual testing experiments to determine the autonomous navigation performance of USVs was established, and the effectiveness of the platform was verified in terms of perception and environmental interference. In virtual environmental interference validation, the average amplitude deviation of the heave motion of USVs under sea state 3 reaches 0.74 m, and the average amplitude deviation of the pitch motion reaches 0.25 rad, showing the gradually increasing disturbance of the sea state. Finally, virtual testing experiments were conducted on a specific USV to evaluate its autonomous navigation perception performance, trajectory tracking performance, and autonomous obstacle avoidance. The evaluation results indicate that the platform can achieve the functionality of virtual testing for the autonomous navigation performance of USVs from the perspective of cost function, taking the reaction distance, regression distance, and obstacle avoidance time into consideration. A representative example is that the cost function deviation rates of overtaking obstacle avoidance between static and dynamic seas reach 5.11%, 8.98% and 18.43%, respectively. The gradually increasing data shows that the virtual simulating method matches the drifting-off-course tendency of boats in rough seas. This includes acquiring perception information of navigation and simulating the motion and navigation processes for visualization. The platform provides new means for testing and evaluating the autonomous navigation performance of USVs. Full article

This paper explores the use of driver-in-the-loop simulations to detect personalized driving styles in autonomous vehicles. The driving simulator used in this study is modular and adaptable, allowing for the testing and validation of control and data-collecting systems, as well as the incorporation and proof of car models. The selected scenario is a double lane change maneuver to overtake a stationary obstacle at a relatively high speed. The user’s behavior was recorded, and lateral accelerations during the maneuver were used as criteria to compare the user-driven vehicle and the autonomous one. The tuning parameters of the lateral and longitudinal controllers were modified to obtain different lateral accelerations of the autonomous vehicle. A neural network was developed to find the combination of the two controllers’ tuning parameters to match the driver’s lateral accelerations in the same double lane change overtaking action. The results are promising, and this study suggests that driver-in-the-loop simulations can help increase autonomous vehicles’ safety while preserving individual driving styles. This could result in creating more individualized and secure autonomous driving systems that consider the preferences and behavior of the driver. Full article

Overtaking is a maneuver that consists of passing another vehicle traveling on the same trajectory, but at a slower speed. Overtaking is considered one of the most dangerous, delicate and complex maneuvers performed by a vehicle, as it requires a quick assessment of the distance and speed of the vehicle to be overtaken, and also the estimation of the available space for the maneuver. In particular, most drivers have difficulty overtaking a vehicle in the presence of vehicles coming on other trajectories. To solve these overtaking problems, this article proposes a method of performing safe, autonomous-vehicle maneuvers through the PreScan simulation program. In this environment, the overtaking-maneuver scenario (OMS) is composed of highway infrastructure, vehicles and sensors. The proposed OMS is based on the solution of minimizing the risks of collision in the presence of any oncoming vehicle during the overtaking maneuver. It is proven that the overtaking maneuver of an autonomous vehicle is safe to perform through the use of advanced driver-assistance systems (ADAS) such as adaptive cruise control (ACC) and technology-independent sensors (TIS) that detect the driving environment of the maneuver. Full article

In motion planning for autonomous racing, the challenge arises in planning smooth trajectories close to the handling limits of the vehicle with a sufficient planning horizon. Graph-based trajectory planning methods can find the global discrete-optimal solution, but they suffer from the curse of dimensionality. Therefore, to achieve low computation times despite a long planning horizon, coarse discretization and simple edges that are efficient to generate must be used. However, the resulting rough trajectories cannot reach the handling limits of the vehicle and are also difficult to track by the controller, which can lead to unstable driving behavior. In this paper, we show that the initial edges connecting the vehicle’s estimated state with the actual graph are crucial for vehicle stability and race performance. We therefore propose a sampling-based approach that relies on jerk-optimal curves to generate these initial edges. The concept is introduced using a layer-based graph, but it can be applied to other graph structures as well. We describe the integration of the curves within the graph and the required adaptation to racing scenarios. Our approach enables stable driving at the handling limits and fully autonomous operation on the race track. While simulations show the comparison of our concept with an alternative approach based on uniform acceleration, we also present experimental results of a dynamic overtake with speeds up to 74 m/s on a full-size vehicle. Full article