基于改进人工势场的无人驾驶动态规划算法研究

摘要

Decision-making algorithms for autonomous driving can be divided into end-to-end and sequential planning algorithms. Sequential algorithms are adopted by most OEMs because of their interpretability and robustness. Planning module is the core of the sequential algorithm. It receives information from a perception module and high-definition map and outputs the driving trajectories or actions. Artificial potential field (APF) method, which is widely used in planning algorithms for autonomous driving, is becoming increasingly popular owing to its excellent planning efficiency and information extraction capability. However, APF does not consider destination factor, and single-point destination gravitational field causes a large force, resulting in incorrect directions for long-distance cases, and cannot cope with complex traffic environments. In response to these problems, this study proposed Driving Intention & Risk Field (IRF) to model traffic factors including destination, vehicles, and road boundaries and consider their characteristics separately and then in combination. A global gravitational field considering the global route was created, and a global planned path was discretized into equidistant path points. The path points within the range of the interest area were dynamically selected to construct a global gravitational field. To verify the performance of the IRF, an IRF-SAC decision-planning algorithm platform was built, and highway, urban crossroad, and roundabout scenes were set in a CARLA simulation environment. The research results show that compared with NF-SAC and FSM, the IRF-SAC algorithm significantly improves safety, comfort, and vehicle-passing efficiency. In the highway scenario, IRF-SAC achieves high accuracy and robustness in path tracking, and the maximum displacement errors are reduced by 44.8% and 70.2% compared with the FSM and NF-SAC algorithms, respectively. In the crossroad scenario, the average risk coefficients are reduced by 12.0% and 20.6%, and root mean squares of the longitudinal acceleration are reduced by 13.2% and 44.9%, compared with the NF-SAC and FSM algorithms, respectively. Moreover, the driving time is reduced by 39.2% compared with the FSM algorithm. In the roundabout scenario, the average risk coefficient is reduced by 31.7% and 52.9%, and the root mean square of the longitudinal acceleration is reduced by 27.0% and 19.0%, compared with the NF-SAC and FSM algorithms, respectively. ? 2022 Xi'an Highway University.

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