A Real-time Spatio-Temporal Trajectory Planner for Autonomous Vehicles with Semantic Graph Optimization

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses. Abstract --Planning a safe and feasible trajectory for autonomous vehicles in real -time by fully utilizing perceptual information in complex urban environments is challenging. In this paper, we propose a spatio -temporal trajectory planning method based on graph optimization. It efficiently extracts the multi -modal information of the perception module by constructing a semantic spatio -temporal map through separation processing of static and dynamic obstacles, and then quickly generates feasible trajectories via sparse graph optimization based on a semantic spatiotemporal hypergraph. Extensive experiments have proven that the proposed method can effectively handle complex urban public road scenarios and perform in real time. HE operation of autonomous vehicle s in a complex urban environment presents great challenges .