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

翻译：在动态环境中实现安全导航仍面临挑战，主要源于障碍物行为的不确定性以及缺乏形式化的预测保证。本文提出两种利用共形预测的运动规划框架：一种全局规划器，其集成了安全区间路径规划算法，用于生成不确定性感知的轨迹；另一种局部规划器，执行在线反应式规划。全局规划器为长时域导航提供无分布的安全保证，而局部规划器通过自适应共形预测缓解障碍物轨迹预测的不准确性，从而在动态环境中实现鲁棒且响应迅速的运动。为进一步提升轨迹可行性，我们在基于共形预测的不确定性量化中引入自适应分位数机制。该机制不采用固定置信水平，而是自动将分位数调整至保持轨迹可行性的最优值，使规划器能在不确定性较高的区域自适应地收紧安全边界。我们通过在动态且杂乱的环境中进行数值实验验证了所提框架的有效性。项目页面详见 https://time-aware-planning.github.io