高精度动态避障与快速规划，让多车竞速更安全高效
Robust Spatiotemporal Motion Planning for Multi-Agent Autonomous Racing via Topological Gap Identification and Accelerated MPC
- 通过拓扑间隙识别预测对手行为，动态生成安全超车路径
- 在密集弯道中超车成功率超81%，总操作时间减少51.6%
- 自研加速求解器支持高频执行，计算延迟降低20.3%
高速多智能体自主竞速要求在严格计算限制下实现鲁棒的时空规划与精准控制。现有方法常过度简化交互或放弃严格的运动学约束。本文提出基于拓扑间隙识别与加速模型预测控制（MPC）的框架。通过结构化高斯过程（SGPs）预测对手行为，构建动态占用走廊，以鲁棒选择最优超车间隙。采用定制化伪瞬态连续（PTC）求解器驱动的线性时变MPC，确保严格运动学可行性，支持高频执行。在F1TENTH平台上实验表明，该方法显著优于当前最优基线：顺序场景下总操作时间减少51.6%，密集瓶颈中持续保持超过81%的超车成功率，平均计算延迟降低20.3%，推动了安全高速自主竞速的边界。
原文摘要 · Abstract (English)
High-speed multi-agent autonomous racing demands robust spatiotemporal planning and precise control under strict computational limits. Current methods often oversimplify interactions or abandon strict kinematic constraints. We resolve this by proposing a Topological Gap Identification and Accelerated MPC framework. By predicting opponent behaviors via SGPs, our method constructs dynamic occupancy corridors to robustly select optimal overtaking gaps. We ensure strict kinematic feasibility using a Linear Time-Varying MPC powered by a customized Pseudo-Transient Continuation (PTC) solver for high-frequency execution. Experimental results on the F1TENTH platform show that our method significantly outperforms state-of-the-art baselines: it reduces total maneuver time by 51.6% in sequential scenarios, consistently maintains an overtaking success rate exceeding 81% in dense bottlenecks, and lowers average computational latency by 20.3%, pushing the boundaries of safe and high-speed autonomous racing.
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