Detecting and Mitigating System-Level Failures of Vision-Based Controllers in Autonomous Systems

Core Concepts

A reachability-based framework is used to automatically mine system-level failures of a vision-based controller, which are then used to train an anomaly detector that can identify inputs likely to cause system breakdowns. A fallback controller is designed to preserve system safety when anomalies are detected.

Abstract

The authors present an approach to detect and mitigate system-level anomalies for autonomous systems that leverage learning-driven vision-based controllers for decision making.
Offline, the authors compute the Backward Reachable Tube (BRT) of the vision-based system using Hamilton-Jacobi Reachability analysis. This allows them to create a labeled dataset of safe and unsafe images without manual intervention. They then train a binary classifier as the anomaly detector using this dataset.
Online, the anomaly detector is used to flag inputs that might cause system breakdowns. When an anomaly is detected, a fallback controller is triggered that trades performance for safety to preserve system safety.
The authors validate their approach on an autonomous aircraft taxiing system that uses a vision-based controller. The results show the efficacy of the proposed approach in identifying and handling system-level anomalies, outperforming methods like prediction error-based detection and ensembling. The inclusion of the anomaly detector and fallback controller significantly reduces the system's Backward Reachable Tube, enhancing the overall safety and robustness of the autonomous system.

Stats

The aircraft's state is represented by the crosstrack error (px), downtrack position (py), and heading error (θ).
The vision-based controller (TaxiNet) returns the estimated crosstrack error (px̂) and heading error (θ̂).
The P-controller then uses these estimates to compute the control input u = tan(-0.74px̂ - 0.44θ̂).
The unsafe states are defined as |px| ≥ B, where B is the runway width.

Quotes

"Seemingly minor errors in the individual modules can cascade into catastrophic effects at a system level. Hence, a system-level view of such problems is often encouraged and is the main motivation behind this work."
"Our key idea is to utilize this [reachability] framework offline to stress-test and automatically mine the system-level failures of the closed-loop system across a variety of environment conditions."

Key Insights Distilled From

Detecting and Mitigating System-Level Anomalies of Vision-Based Controllers

by Aryaman Gupt... at arxiv.org 04-10-2024

https://arxiv.org/pdf/2309.13475.pdf

Deeper Inquiries

How can the proposed anomaly detection and mitigation framework be extended to handle dynamic environments where the system's operating conditions change over time

To extend the proposed anomaly detection and mitigation framework to handle dynamic environments, where the system's operating conditions change over time, several key adaptations can be implemented:

Dynamic Model Updating: Incorporating mechanisms to update the anomaly detection model in real-time based on the evolving system dynamics and environmental conditions. This can involve continuous retraining of the anomaly detector using incoming data to adapt to changing scenarios.

Adaptive Thresholding: Implementing adaptive thresholding techniques that can dynamically adjust the anomaly detection thresholds based on the current operating conditions. This ensures that the system remains sensitive to anomalies even as the environment changes.

Temporal Analysis: Introducing temporal analysis methods to consider the historical context of anomalies and system behavior. By analyzing trends over time, the framework can better anticipate and respond to emerging anomalies in dynamic environments.

Multi-Modal Fusion: Integrating multi-modal sensor data fusion techniques to capture a more comprehensive view of the environment. By combining data from different sensors, the framework can enhance anomaly detection accuracy in dynamic settings.

Reinforcement Learning: Leveraging reinforcement learning algorithms to enable the system to learn and adapt its anomaly detection and mitigation strategies based on feedback from the environment. This adaptive learning approach can improve the framework's performance in dynamic scenarios.

What are the potential limitations of the reachability-based approach in capturing all possible system-level failures, and how can these be addressed

The reachability-based approach, while effective in capturing system-level failures, may have certain limitations that need to be addressed:

Complexity of System Dynamics: Reachability analysis may struggle with highly complex system dynamics or non-linear behaviors that are challenging to model accurately. Addressing this limitation requires exploring advanced modeling techniques or incorporating additional system dynamics information.

Limited Environmental Representation: The reachability-based approach may not fully capture all possible environmental variations that could lead to system failures. Enhancements such as incorporating more diverse environmental conditions in the training dataset can help address this limitation.

Sensitivity to Model Assumptions: The reachability analysis relies on certain assumptions about the system and its dynamics, which may not always hold true in practical scenarios. Conducting sensitivity analyses and robustness checks can help mitigate the impact of these assumptions on the results.

Scalability Issues: Scaling the reachability-based approach to larger and more complex systems may pose challenges in terms of computational resources and time. Exploring parallel computing techniques or optimization strategies can help overcome scalability limitations.

Can the insights gained from the system-level anomaly detection be used to improve the underlying vision-based controller's robustness, beyond just triggering a fallback mechanism

Insights gained from system-level anomaly detection can indeed be leveraged to enhance the underlying vision-based controller's robustness in several ways:

Data Augmentation: Using the detected anomalies as additional training data, the vision-based controller can be retrained to better handle similar failure scenarios in the future. This process enhances the controller's ability to recognize and respond to potential failures proactively.

Model Refinement: Analyzing the failure modes identified by the anomaly detection system can provide valuable feedback for refining the vision-based controller's algorithms and improving its performance in challenging conditions. This iterative process of model refinement based on detected anomalies enhances the controller's robustness.

Feature Engineering: Extracting key features from the anomalies detected by the system-level analysis can help in identifying specific patterns or characteristics that indicate potential failures. Integrating these features into the vision-based controller's decision-making process can enhance its ability to preemptively address system-level anomalies.

Continuous Monitoring: Implementing a feedback loop where the anomaly detection system continuously monitors the controller's performance and provides real-time feedback can enable adaptive adjustments to the controller's parameters or strategies. This continuous monitoring and adaptation mechanism contribute to the controller's overall robustness and resilience to system-level failures.