Time-aware Motion Planning in Dynamic Environments with Conformal Prediction

2025-11-22

AI Summary

To address the challenges of high uncertainty in obstacle behavior and the lack of formal safety guarantees in dynamic environments, this paper proposes a hierarchical motion planning framework integrated with conformal prediction. At the global level, an adaptive quantile mechanism dynamically adjusts prediction confidence to enable distribution-free safe trajectory planning; at the local level, Safe Interval Path Planning (SIPP) is coupled with online reactive planning to support risk-aware real-time replanning. The key contribution lies in introducing an environment-adaptive uncertainty quantification mechanism that automatically balances safety and flexibility without distributional assumptions. Experimental results demonstrate that the method significantly improves trajectory feasibility and safety in complex dynamic scenarios, while providing both theoretically provable safety guarantees and practical robustness.

Technology Category

• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Intelligent Robots: Motion and Path Planning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

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

Problem

• Addressing uncertain obstacle behaviors in dynamic navigation
• Providing formal safety guarantees for motion planning
• Enhancing trajectory feasibility with adaptive uncertainty quantification

Innovation

• Leverages conformal prediction for uncertainty-aware trajectory generation
• Integrates global Safe Interval Path Planning with local reactive planning
• Uses adaptive quantile mechanism to automatically tighten safety margins