Tackling Occlusions & Limited Sensor Range with Set-based Safety Verification

TL;DR

This paper introduces a set-based safety verification method for automated driving that accounts for occlusions and limited sensor range, providing provable safety guarantees for trajectories in complex environments.

Contribution

It extends set-based methods to verify safety of trajectories considering occlusions and unknown obstacle states, enabling provably safe trajectory planning.

Findings

Verifies safety of trajectories under occlusions.

Over-approximates obstacle states with intervals.

Demonstrates safety verification in three scenarios.

Abstract

Provable safety is one of the most critical challenges in automated driving. The behavior of numerous traffic participants in a scene cannot be predicted reliably due to complex interdependencies and the indiscriminate behavior of humans. Additionally, we face high uncertainties and only incomplete environment knowledge. Recent approaches minimize risk with probabilistic and machine learning methods - even under occlusions. These generate comfortable behavior with good traffic flow, but cannot guarantee safety of their maneuvers. Therefore, we contribute a safety verification method for trajectories under occlusions. The field-of-view of the ego vehicle and a map are used to identify critical sensing field edges, each representing a potentially hidden obstacle. The state of occluded obstacles is unknown, but can be over-approximated by intervals over all possible states. Then set-based methods are extended to provide occupancy predictions for obstacles with state intervals. The proposed method can verify the safety of given trajectories (e.g. if they ensure collision-free fail-safe maneuver options) w.r.t. arbitrary safe-state formulations. The potential for provably safe trajectory planning is shown in three evaluative scenarios.