Spatial-temporal risk field-based coupled dynamic-static driving risk assessment and trajectory planning in weaving...

As connected and automated vehicles (CAVs) gradually penetrate the existing transportation system, the inherent turbulence within weaving segments is expected to be mitigated through CAV technologies. However, despite making progress in capturing static and dynamic risk factors, traditional CAV technologies still lack foreseeability for dynamic risks. This leads to suboptimal results in trajectory planning, thereby hindering the maximization of expected benefits. To fill these gaps, we first propose a spatial-temporal coupled risk assessment paradigm by constructing a three-dimensional spatial-temporal risk field (STRF). Specifically, we introduce spatial-temporal distances to quantify the impact of future trajectories of dynamic obstacles. We also incorporate a geometrically configured specialized field for weaving segments to constrain vehicle movement directionally. To enhance the STRF's accuracy, we further developed a parameter calibration method using real-world aerial video data, leveraging YOLO-based machine vision and dynamic risk balance theory. A comparative analysis with traditional risk field shows that the STRF possesses superior risk foreseeability. Building on these results, we final design a STRF-based CAV trajectory planning method in weaving segments. We integrate spatial-temporal risk occupancy maps, dynamic iterative sampling, and quadratic programming to enhance safety, comfort, and efficiency. By incorporating both dynamic and static risk factors during the sampling phase, our method ensures robust safety performance. Additionally, the proposed method simultaneously optimizes path and speed using a parallel computing approach, reducing computation time. Real-world cases show that, compared to the baseline schemes, and real human driving trajectories, our method significantly improves safety, reduces lane-change completion time, and minimizes speed fluctuations.