Search Results (4)

Improving the accuracy of parking space recognition is crucial in the fields for Automated Valet Parking (AVP) of autonomous driving. In AVP, accurate free space recognition significantly impacts the safety and comfort of both the vehicles and drivers. To enhance parking space recognition and annotation in unknown environments, this paper proposes an automatic parking space annotation approach with tight coupling of Lidar and Inertial Measurement Unit (IMU). First, the pose of the Lidar frame was tightly coupled with high-frequency IMU data to compensate for vehicle motion, reducing its impact on the pose transformation of the Lidar point cloud. Next, simultaneous localization and mapping (SLAM) were performed using the compensated Lidar frame. By extracting two-dimensional polarized edge features and planar features from the three-dimensional Lidar point cloud, a polarized Lidar odometry was constructed. The polarized Lidar odometry factor and loop closure factor were jointly optimized in the iSAM2. Finally, the pitch angle of the constructed local map was evaluated to filter out ground points, and the regions of interest (ROI) were projected onto a grid map. The free space between adjacent vehicle point clouds was assessed on the grid map using convex hull detection and straight-line fitting. The experiments were conducted on both local and open datasets. The proposed method achieved an average precision and recall of 98.89% and 98.79% on the local dataset, respectively; it also achieved 97.08% and 99.40% on the nuScenes dataset. And it reduced storage usage by 48.38% while ensuring running time. Comparative experiments on open datasets show that the proposed method can adapt to various scenarios and exhibits strong robustness. Full article

Automated vehicles (AVs) are recognized as one of the most effective measures to realize sustainable transport. These vehicles can reduce emissions and environmental pollution, enhance accessibility, improve safety, and produce economic benefits through congestion reduction and cost savings. However, the consumer acceptance of and trust in these vehicles are not ideal, which affects the diffusion speed of AVs on the market. Providing transparent explanations of AV behaviour is a method for building confidence and trust in AV technologies. In this study, we investigated the explainability of user interface information in an Automated Valet Parking (AVP) system—one of the first L4 automated driving systems with a large commercial landing. Specifically, we proposed a scenario-based explanation framework based on explainable AI and examined the effects of these explanations on drivers’ objective and subjective performance. The results of Experiment 1 indicated that the scenario-based explanations effectively improved drivers’ situational trust and user experience (UX), thereby enhancing the perception and understanding that drivers had of the system’s intelligence capabilities. These explanations significantly reduced the mental workload and elevated the user performance in objective evaluations. In Experiment 2, we uncovered distinct explainability preferences among new and frequent users. New users sought increased trust and transparency, benefiting from guided explanations. In contrast, frequent users emphasised efficiency and driving safety. The final experimental results confirmed that solutions customised for different segments of the population are significantly more effective, satisfying, and trustworthy than generic solutions. These findings demonstrate that the explanations for individual differences, based on our proposed scenario-based framework, have significant implications for the adoption and sustainability of AVs. Full article

This paper explores the minimum lateral parking distance and parking acceleration/deceleration distance of vehicles to improve the efficiency of automated valet parking (AVP) lots and save urban land. Specifically, the paper focuses on designing parking lots for automated guided vehicles (AGVs) and their parking attributes. To ensure AGV accessibility and maximize AVP capacity, graph theories and unique path-driving methods are used in designing mobile priority parking lots and decision spaces. Additionally, the paper proposes an optimization design for parking lots with obstacles, considering the layout of load-bearing columns and charging resources for electric vehicles in underground parking lots. The article further proposes an optimization design for hybrid parking lots based on spatio-temporal resource conversion in traffic design and the principle of traffic separation in traffic control since hybrid parking lots that accommodate both conventional vehicles and AGVs are crucial to the future development of urban parking lots. The experimental results show that the proposed optimization design for urban parking lots in automated environments is superior to the traditional parking lots design in terms of capacity and density. This paper provides an optimal layout scheme of urban parking lots in multiple scenarios, which can improve the service level of urban static traffic systems. Full article

Autonomous driving helps drivers avoid paying attention to keeping to a lane or keeping a distance from the vehicle ahead. However, the autonomous driving is limited by the need to park upon the completion of driving. In this sense, automated valet parking (AVP) system is one of the promising technologies for enabling drivers to free themselves from the burden of parking. Nevertheless, the driver must continuously monitor the automated system in the current automation level. The main reason for monitoring the automation system is due to the limited sensor range and occlusions. For safety reasons, the current field of view must be taken into account, as well as to ensure comfort and to avoid unexpected and harsh reactions. Unfortunately, due to parked vehicles and structures, the field of view in a parking lot is not sufficient for considering new obstacles coming out of occluded areas. To solve this problem, we propose a method that estimates the risks for unobservable obstacles by considering worst-case assumptions. With this method, we can ensure to not act overcautiously while moving safe. As a result, the proposed method can be a proactive approach to consider the limited visibility encountered in a parking lot. In the proposed method, occlusion can be efficiently reflected in the planning process. The potential of the proposed method is evaluated in a variety of simulations. Full article