Occlusion-Aware Contingency Safety-Critical Planning for Autonomous Vehicles

2025-02-10

AI Summary

Addressing the challenge of simultaneously ensuring safety and efficiency for autonomous vehicles under dynamic occlusions, this paper proposes an emergency-oriented dual-trajectory cooperative planning framework. To model motion risks posed by “phantom vehicles” in occluded regions, we employ reachability analysis and jointly optimize exploratory and fallback trajectories within a receding-horizon control framework. We introduce the first occlusion-aware doubly convex nonlinear programming formulation, explicitly embedding time-varying velocity constraints into the optimization structure. Furthermore, we design a consensus-based alternating direction method of multipliers (ADMM) solver that guarantees convergence and achieves millisecond-level real-time performance. Extensive simulations at occluded intersections and real-world vehicle experiments demonstrate significant improvements in traffic throughput and collision avoidance capability. The method exhibits strong robustness under complex, heterogeneous obstacle distributions and validates its engineering feasibility for practical deployment.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Non-convex Optimization
• Intelligent Robots: Motion and Path Planning

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Ensuring safe driving while maintaining travel efficiency for autonomous vehicles in dynamic and occluded environments is a critical challenge. This paper proposes an occlusion-aware contingency safety-critical planning approach for real-time autonomous driving in such environments. Leveraging reachability analysis for risk assessment, forward reachable sets of occluded phantom vehicles are computed to quantify dynamic velocity boundaries. These velocity boundaries are incorporated into a biconvex nonlinear programming (NLP) formulation, enabling simultaneous optimization of exploration and fallback trajectories within a receding horizon planning framework. To facilitate real-time optimization and ensure coordination between trajectories, we employ the consensus alternating direction method of multipliers (ADMM) to decompose the biconvex NLP problem into low-dimensional convex subproblems. The effectiveness of the proposed approach is validated through simulation studies and real-world experiments in occluded intersections. Experimental results demonstrate enhanced safety and improved travel efficiency, enabling real-time safe trajectory generation in dynamic occluded intersections under varying obstacle conditions. A video showcasing the experimental results is available at https://youtu.be/CHayG7NChqM.

Problem

• Safe planning for autonomous vehicles
• Real-time trajectory optimization
• Handling occluded dynamic environments

Innovation

• Occlusion-aware contingency planning
• Biconvex NLP for trajectory optimization
• ADMM for real-time problem decomposition