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Hierarchical Motion Planning for Autonomous Driving in Large-Scale Complex Scenarios

Motion planning algorithms, an essential part of the autonomous driving system, have been extensively studied. However, in large-scale complex scenarios, how to develop an optimal path to comply with the requirements of smoothness and safety remains a vital issue. In this study, a hierarchical search spacial scales-based hybrid A* (termed as HHA*) motion planning method is proposed, capable of efficiently generating smooth and safe paths. The proposed HHA* method covers two stages. First, the search space is divided on a coarse scale to generate local goals. Subsequently, the novel heuristic function and exploration strategies are adopted in the fine-scale search space to generate paths like that with a human driver guided by the local goals. Moreover, with the usage of the clothoid, the smoothness of the generated path is improved to be G2–continuous (i.e., curvature continuous), which fits the vehicle’s kinematic constraints without the need for later smoothing. Numerous experimental results from the simulation and on-road tests indicate that the proposed method can effectively perform motion planning that meets smoothness and safety in large-scale complex scenarios.