Designing Safe AI-Based Autonomous Systems: A Harmonious Blend of Machine Learning and Safety

Summary

Autonomous systems based on machine learning (ML), such as self-driving cars, drones, and robots, are rapidly proliferating across various fields. However, safety remains a critical challenge. This content proposes a novel framework for systematically integrating safety throughout the entire lifecycle of autonomous systems—from design and operation to continuous improvement. Specifically, it leverages neural safety representation based on Hamilton-Jacobi reachability analysis to train data-driven controllers, adapt to real-time environmental changes, and proactively identify system vulnerabilities. This approach offers new possibilities for developing autonomous systems that maintain the advantages of ML while ensuring safety.

Key Points

• Safety-First Design of ML-Based Controllers: Instead of relying on post-hoc safety measures ("safety bandages"), this framework formally integrates safety requirements into the design and training phases. Hamilton-Jacobi reachability analysis is used to mathematically define the safe set and safe controller, which are then approximated by neural networks for applicability to high-dimensional systems.
• Adaptive Systems for Real-Time Safety Assurance: This framework addresses changing environmental conditions (wind speed, obstacles) and system uncertainties (slippery surfaces) by adaptively adjusting the safe controller in real-time. This is achieved using parameter-conditioned safety value functions and observation-conditioned reachable sets.
• System Vulnerability Analysis and Improvement: This framework presents a methodology for evaluating the safety of learned policies and identifying vulnerabilities. Reachability analysis is used to pinpoint visual inputs (e.g., misidentification of runway markings) that can lead to system failure. This information is then used to design anomaly detectors and alternative controllers to enhance safety.
• Safety-Aware Imitation Learning: To address the issue of safety not being guaranteed in imitation learning even with safe demonstration data, this framework incorporates adversarial disturbances during data collection to acquire data representing unsafe conditions, thereby training more robust and safer policies.

Details

This content presents a novel approach to addressing the safety challenges of autonomous systems. Existing ML-based autonomous systems primarily focus on performance improvement, often addressing safety issues reactively. However, this approach increases safety risks and makes it difficult to handle diverse situations. For example, Waymo's approach of halting all vehicle operations and updating software after a collision with a tow truck lacks scalability and can lead to performance degradation.

This content proposes a new framework that integrates safety throughout the entire system design process. The core element is neural safety representation based on Hamilton-Jacobi reachability analysis (a method for calculating the safe set and safe controller considering system dynamics and safety constraints). This method approximates the safety value function with a neural network, enabling efficient design of safe controllers even in high-dimensional systems. The key is training the safety value function to satisfy the Hamilton-Jacobi equation through a self-supervised learning method called DeepReach.

Furthermore, this content presents a method for implementing a real-time adaptive safety system using parameter-conditioned safety value functions and observation-conditioned reachable sets. For instance, in a drone delivery system, the safe region can be dynamically adjusted based on changing wind speeds, and in robotics, the safety value function can be updated in real-time based on LiDAR scan data to avoid unknown obstacles. The framework also presents methods for estimating system uncertainties and adjusting the safe controller more conservatively.

Finally, this content presents a methodology for evaluating the safety of learned policies and identifying vulnerabilities. Policy-conditioned reachability analysis is used to identify input data that can lead to system failure, which is then used to design anomaly detectors and alternative controllers to improve safety. Specifically, for vision-based controllers, the framework analyzes system failures due to visual information errors and proposes methods for improvement. It also introduces methods for improving the safety of imitation learning using safety information and defining safety constraints using natural language feedback.

Implications

This content offers a comprehensive solution to the safety challenges of ML-based autonomous systems. The novel framework and practical methodologies for integrating safety into all stages of system design are expected to significantly contribute to the development of safe and reliable autonomous systems in various fields, including autonomous driving, drones, and robotics. Specifically, the neural safety representation, real-time adaptive system, and system vulnerability analysis and improvement methods provide immediately applicable insights. However, computational complexity in high-dimensional systems, computational resource constraints for real-time safety assurance, and the definition and management of safety constraints remain challenges for future research. Developing a quantitative evaluation and management system for various safety levels is also crucial. Through continued research and development addressing these challenges, we can accelerate the arrival of a safer and more reliable era of autonomous systems.