System-Level Safety Monitoring and Recovery for Perception Failures in Autonomous Vehicles

TL;DR

This paper introduces SPARQ, a system-level safety evaluation Q-network for autonomous vehicles that assesses and corrects perception-related safety risks in real-time, improving safety assurance during operation.

Contribution

The work presents a novel Q-network-based approach for real-time safety evaluation and recovery in AVs, addressing perception failures at the system level.

Findings

Achieves 90% accuracy and recall in safety assessment

Operates at 42Hz on unseen datasets

Outperforms reachability-based baseline in safety evaluation

Abstract

The safety-critical nature of autonomous vehicle (AV) operation necessitates development of task-relevant algorithms that can reason about safety at the system level and not just at the component level. To reason about the impact of a perception failure on the entire system performance, such task-relevant algorithms must contend with various challenges: complexity of AV stacks, high uncertainty in the operating environments, and the need for real-time performance. To overcome these challenges, in this work, we introduce a Q-network called SPARQ (abbreviation for Safety evaluation for Perception And Recovery Q-network) that evaluates the safety of a plan generated by a planning algorithm, accounting for perception failures that the planning process may have overlooked. This Q-network can be queried during system runtime to assess whether a proposed plan is safe for execution or poses potential safety risks. If a violation is detected, the network can then recommend a corrective plan while accounting for the perceptual failure. We validate our algorithm using the NuPlan-Vegas dataset, demonstrating its ability to handle cases where a perception failure compromises a proposed plan while the corrective plan remains safe. We observe an overall accuracy and recall of 90% while sustaining a frequency of 42Hz on the unseen testing dataset. We compare our performance to a popular reachability-based baseline and analyze some interesting properties of our approach in improving the safety properties of an AV pipeline.