Exact, Efficient, and Safe Occlusion-Aware Planning Using AH-Polyhedrons

2026-06-12

AI Summary

This work addresses the challenge of safe motion planning for autonomous mobile robots in dynamically occluded environments, where existing approaches either lack formal safety guarantees, rely on assumptions about road structure, or become overly conservative and fail to complete tasks. The authors propose the APRO framework, which for the first time precisely models occlusion-aware safety conditions as a union of AH-polytopes. By integrating game-theoretic active perception with reachability analysis, APRO enables safety verification via linear programming without introducing unnecessary conservatism, while supporting real-time optimal planning. Notably, the method operates without assumptions on road topology and achieves 100% safety rates across extensive evaluations—including simulations, hardware experiments, and real-world parking lot replays—outperforming existing safety-guaranteed planners in both optimality and computational efficiency.

Abstract

Safely handling occlusions is a fundamental challenge for autonomous mobile robots operating in dynamic environments. This issue is especially prominent in autonomous valet parking (AVP), where traffic rules are lax, occlusions are frequent and cluttered, and overly conservative behavior can leave vehicles stuck. However, existing methods either lack formal safety guarantees, assume agents follow road structures, or introduce conservatism, leaving occlusion-aware planning for AVP an open challenge. In this paper, we propose APRO (AH-Polyhedron Reachability for Occlusions), an exact and efficient occlusion-aware planning framework based on game-theoretic active perception and AH-polyhedron reachability analysis with AVP as our canonical use case. Our key insight is to reformulate set-based safety conditions in prior work as unions of AH-polyhedrons, enabling exact safety verification through linear programming (LP) without any additional conservatism in set computations or assumptions on road topology. We further show how the resulting safety conditions can be integrated into optimization-based planners or a bisection search scheme for real-time applications. We validate our method in simulation and hardware experiments, including data replay on a real-world parking lot dataset. Experimental results demonstrate that our method consistently achieved a 100% safety rate across all evaluated scenarios while maintaining real-time performance, resulting in safer and more optimal decisions than existing methods with formal safety guarantees.

Problem

• occlusion-aware planning
• autonomous valet parking
• safety guarantees
• dynamic environments
• mobile robots

Innovation

• AH-Polyhedron
• occlusion-aware planning
• reachability analysis
• formal safety guarantees
• autonomous valet parking