Safe Occlusion-aware Autonomous Driving via Game-Theoretic Active Perception

TL;DR

This paper presents a game-theoretic approach to safe trajectory planning for autonomous vehicles that accounts for occlusions and guarantees collision avoidance by modeling interactions with hidden traffic participants.

Contribution

It introduces a hybrid zero-sum dynamic game framework for occlusion-aware safety analysis and integrates it into a trajectory planning method with worst-case guarantees.

Findings

Guarantees collision avoidance under occlusions in urban and highway scenarios.

Provides a less conservative, active perception-based safety framework.

Validated using the CARLA simulator in diverse driving conditions.

Abstract

Autonomous vehicles interacting with other traffic participants heavily rely on the perception and prediction of other agents' behaviors to plan safe trajectories. However, as occlusions limit the vehicle's perception ability, reasoning about potential hazards beyond the field of view is one of the most challenging issues in developing autonomous driving systems. This paper introduces a novel analytical approach that poses safe trajectory planning under occlusions as a hybrid zero-sum dynamic game between the autonomous vehicle (evader) and an initially hidden traffic participant (pursuer). Due to occlusions, the pursuer's state is initially unknown to the evader and may later be discovered by the vehicle's sensors. The analysis yields optimal strategies for both players as well as the set of initial conditions from which the autonomous vehicle is guaranteed to avoid collisions. We leverage this theoretical result to develop a novel trajectory planning framework for autonomous driving that provides worst-case safety guarantees while minimizing conservativeness by accounting for the vehicle's ability to actively avoid other road users as soon as they are detected in future observations. Our framework is agnostic to the driving environment and suitable for various motion planners. We demonstrate our algorithm on challenging urban and highway driving scenarios using the open-source CARLA simulator.