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arXiv · 2409.17630

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

Kaustav Chakraborty·Zeyuan Feng·Sushant Veer·Apoorva Sharma·Boris Ivanovic·Marco Pavone·Somil Bansal
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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.

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Kaustav Chakraborty, Zeyuan Feng, Sushant Veer, Apoorva Sharma, Boris Ivanovic, Marco Pavone, Somil Bansal. 2024-09-26. System-Level Safety Monitoring and Recovery for Perception Failures in Autonomous Vehicles. https://arxiv.org/abs/2409.17630

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