用置信预测提升动态环境中的安全导航能力
Time-aware Motion Planning in Dynamic Environments with Conformal Prediction
- 结合置信预测与路径规划，实现无分布假设的安全轨迹生成
- 自适应调整置信水平，确保高不确定性区域的轨迹可行性
- 适合需要可靠安全保证的自动驾驶与机器人导航场景
动态环境中安全导航仍面临障碍物行为不确定及缺乏正式预测保障的挑战。本文提出两种基于置信预测（Conformal Prediction, CP）的运动规划框架：全局规划器融合安全区间路径规划（SIPP），生成具有不确定性感知的轨迹；局部规划器则通过在线自适应CP修正障碍物轨迹预测误差，实现鲁棒响应。为提升轨迹可行性，引入自适应分位数机制，在不固定置信水平的前提下，自动调节至最优值以保持轨迹可行，从而在高不确定性区域动态收紧安全裕度。在动态复杂环境中的数值实验验证了该框架的有效性。
原文摘要 · Abstract (English)
Safe navigation in dynamic environments remains challenging due to uncertain obstacle behaviors and the lack of formal prediction guarantees. We propose two motion planning frameworks that leverage conformal prediction (CP): a global planner that integrates Safe Interval Path Planning (SIPP) for uncertainty-aware trajectory generation, and a local planner that performs online reactive planning. The global planner offers distribution-free safety guarantees for long-horizon navigation, while the local planner mitigates inaccuracies in obstacle trajectory predictions through adaptive CP, enabling robust and responsive motion in dynamic environments. To further enhance trajectory feasibility, we introduce an adaptive quantile mechanism in the CP-based uncertainty quantification. Instead of using a fixed confidence level, the quantile is automatically tuned to the optimal value that preserves trajectory feasibility, allowing the planner to adaptively tighten safety margins in regions with higher uncertainty. We validate the proposed framework through numerical experiments conducted in dynamic and cluttered environments. The project page is available at https://time-aware-planning.github.io
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