基于语义图优化的实时轨迹规划，提升复杂城市道路的自主驾驶安全性
A Real-time Spatio-Temporal Trajectory Planner for Autonomous Vehicles with Semantic Graph Optimization
- 分离静态与动态障碍物构建语义时空图，融合多模态感知信息
- 通过稀疏图优化快速生成可行轨迹，满足实时性要求
- 适用于复杂城市道路场景，代码开源便于研究复现
在复杂城市道路环境中，实时充分利用感知信息规划安全可行的自动驾驶车辆轨迹仍具挑战。本文提出一种基于图优化的时空轨迹规划方法，通过分离处理静态与动态障碍物，构建语义时空地图，高效提取感知模块的多模态信息，并基于语义时空超图实现稀疏图优化，快速生成可行轨迹。大量实验表明，该方法能有效应对复杂城市公共道路场景，具备实时性能。我们还将开源代码，以支持研究社区的基准测试。
原文摘要 · Abstract (English)
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
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