Detecting and Mitigating System-Level Anomalies of Vision-Based Controllers
This work addresses safety concerns for autonomous systems like self-driving cars and drones by improving robustness against out-of-distribution inputs, though it is incremental as it builds on existing anomaly detection and reachability frameworks.
The paper tackles the problem of vision-based controllers in autonomous systems making errors on novel inputs, which can lead to catastrophic failures, by introducing a run-time anomaly monitor that detects and mitigates these system-level issues, showing efficacy in an autonomous aircraft taxiing system and outperforming methods like prediction error-based detection.
Autonomous systems, such as self-driving cars and drones, have made significant strides in recent years by leveraging visual inputs and machine learning for decision-making and control. Despite their impressive performance, these vision-based controllers can make erroneous predictions when faced with novel or out-of-distribution inputs. Such errors can cascade to catastrophic system failures and compromise system safety. In this work, we introduce a run-time anomaly monitor to detect and mitigate such closed-loop, system-level failures. Specifically, we leverage a reachability-based framework to stress-test the vision-based controller offline and mine its system-level failures. This data is then used to train a classifier that is leveraged online to flag inputs that might cause system breakdowns. The anomaly detector highlights issues that transcend individual modules and pertain to the safety of the overall system. We also design a fallback controller that robustly handles these detected anomalies to preserve system safety. We validate the proposed approach on an autonomous aircraft taxiing system that uses a vision-based controller for taxiing. Our results show the efficacy of the proposed approach in identifying and handling system-level anomalies, outperforming methods such as prediction error-based detection, and ensembling, thereby enhancing the overall safety and robustness of autonomous systems.