A Real-time Spatio-Temporal Trajectory Planner for Autonomous Vehicles with Semantic Graph Optimization
This addresses the challenge of safe and feasible autonomous driving planning for vehicles in dynamic urban settings, representing an incremental improvement in trajectory planning methods.
The paper tackles real-time trajectory planning for autonomous vehicles in complex urban environments by proposing a spatio-temporal method using semantic graph optimization, achieving effective handling of public road scenarios with real-time performance.
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 spatio-temporal hypergraph. Extensive experiments have proven that the proposed method can effectively handle complex urban public road scenarios and perform in real time. We will also release our codes to accommodate benchmarking for the research community