Occlusion-Aware Contingency Safety-Critical Planning for Autonomous Driving

TL;DR

The paper tackles safe and efficient autonomous driving in occluded environments by introducing an occlusion-aware contingency planner that combines reachability-based risk assessment with dual-trajectory planning. It formulates a biconvex NLP that couples exploration and safety-fallback trajectories under spatiotemporal barriers, solved in real time via consensus ADMM with separable subproblems and an augmented Lagrangian. The method demonstrates improved travel efficiency and safety in simulations and real-world 1:10 scale experiments, achieving rapid replanning (tens of milliseconds) while maintaining a shared initial trajectory segment for smooth transitions. This approach offers a scalable, real-time solution for occlusion-rich urban driving, with practical implications for deploying safety-critical planning in dense traffic, albeit with limitations around pedestrian interactions and unstructured scenarios.

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. Leveraging reachability analysis for risk assessment, forward reachable sets of phantom vehicles are used to derive risk-aware dynamic velocity boundaries. These velocity boundaries are incorporated into a biconvex nonlinear programming (NLP) formulation that formally enforces safety using spatiotemporal barrier constraints, while simultaneously optimizing exploration and fallback trajectories within a receding horizon planning framework. To enable real-time computation and 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 simulations 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. The project page is available at https://zack4417.github.io/oacp-website/.

Figures (9)

• Figure 1: The EV (red) navigates an occluded four-way intersection, generating two trajectories: a blue exploration trajectory and a red fallback trajectory with an arrow. Both trajectories share an initial common segment to enable smooth transitions between trajectories. Visibility is obstructed by static obstacles (buildings) and dynamic vehicles (orange), highlighting the challenges of safe and efficient driving in occluded environments.
• Figure 2: Spatial distribution of risk around the EV in the presence of occlusion. The EV is positioned at [$0\,\text{m}$, $-2\,\text{m}$], with the occluded area extending from [$-8\,\text{m}$, $0\,\text{m}$] to [$-2\,\text{m}$, $0\,\text{m}$]. The quantitative color scale (right), labeled 'Risk Value', illustrates the risk level, where warmer colors (e.g., red) represent higher risk and cooler colors (e.g., blue) indicate lower risk.
• Figure 3: A top-down view of the EV navigating through a dynamic and occluded intersection under dense traffic conditions. The blue vehicles (SV3, SV4) represent non-interacting vehicles whose future trajectories do not intersect with the EV. The lighter orange vehicles (SV5, SV6) are occluded, while the solid orange vehicles (SV1, SV2) are fully visible. The EV plans two potential trajectories to manage the occlusion risks: the black exploration trajectory prioritizes travel efficiency, while the conservative fallback trajectory accounts for higher occlusion risks.
• Figure 4: Third-person view snapshots of the red EV navigating through a dense and occluded intersection at different time instants. The proposed occlusion-aware contingency planner enables the EV to first decelerate to avoid potential hazards and then accelerate to safely pass through the occluded intersection under dense traffic conditions.
• Figure 5: The generated velocity profiles of two trajectories at time instant $7\,\text{s}$. Two trajectories show the shared initial segment and the separate process for safe navigation.

Theorems & Definitions (7)

• Definition 1: Reachable Sets: Forward and Backward
• Remark 1
• Remark 2
• Remark 3
• Remark 4
• Remark 5
• Remark 6