提出新框架提升高速超车轨迹规划效率与成功率
An Overtaking Trajectory Planning Framework Based on Spatio-temporal Topology and Reachable Set Analysis Ensuring Time Efficiency
- 用时空拓扑分类超车行为，生成多样初始路径避免局部最优
- 通过可达集并行评估，计算速度提升62.9%，轨迹更平滑
- 适配自动驾驶赛车平台，实测100圈挑战场景表现稳健
在高速场景中生成超车轨迹通常采用分层规划，但常因初始解单一导致陷入局部最优，且数值优化计算效率低。本文提出一种融合时空拓扑与可达集分析的超车轨迹规划框架（SROP）。上层规划通过引入拓扑类别表征不同超车行为，进行时空搜索以提取多样初始路径，有效避免局部最优。下层规划利用可达集并行评估轨迹，将车辆运动学约束解耦于优化过程，确保可行性并显著加速计算。数值实验表明，相较于现有最优方法，SROP使轨迹平滑度提升66.8%，计算时间减少62.9%。进一步将方法无缝集成至F1TENTH自动驾驶赛车仿真平台，100圈敏感性分析显示其在复杂场景中具备高超车成功率，验证了该方法的实际应用价值、实时效率与鲁棒性。
原文摘要 · Abstract (English)
Generating overtaking trajectories in high-speed scenarios is typically addressed through hierarchical planning, which often suffers from local optima due to single initial solutions and low computational efficiency during numerical optimization. To overcome these limitations, this paper proposes a Spatio-temporal topology and Reachable set analysis enhanced Overtaking trajectory Planning framework (SROP). Specifically, by introducing topological classes to represent distinct overtaking behaviors, the upper-layer planner performs a spatio-temporal search to extract diverse initial paths, effectively preventing local optima. Subsequently, a lower-layer planner conducts parallel trajectory evaluation using reachable sets, which decouples vehicle kinematic constraints from the optimization process to ensure feasibility and significantly accelerate computation. Numerical experiments demonstrate that SROP improves trajectory smoothness by 66.8% and reduces computation time by 62.9% compared to state-of-the-art methods. Furthermore, by seamlessly integrating the method into the F1TENTH autonomous racing simulation platform, a 100-lap sensitivity analysis demonstrates high overtaking success rates in challenging scenarios, thereby validating its practical utility, real-time efficiency, and robustness.
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