An Efficient Sampling-Based Hybrid A? Algorithm for Intelligent Vehicles

In this paper, we propose an improved sampling-based hybrid A? (SBA?) algorithm for path planning of intelligent vehicles, which works efficiently in complex urban environments. Two main modifications are introduced into the traditional hybrid A? algorithm to improve its adaptivity in both structured and unstructured traffic scenes. Firstly, a hybrid potential field (HPF) model considering both traffic regulation and obstacle configuration is proposed to represent the vehicle's workspace, which is utilized as a heuristic function. Secondly, a set of directional motion primitives is generated by taking the prior topological structure of the workspace into account. The path planner using SBA? not only obeys traffic regulations in structured scenes but also is capable of exploring complex unstructured scenes rapidly. Finally, a post-optimization step is adopted to increase the feasibility of the path. The efficacy of the proposed algorithm is extensively validated and tested with an autonomous vehicle in real traffic scenes. The experimental results show that SBA? works well in complex urban environments.