Formal Test Synthesis for Safety-Critical Autonomous Systems based on Control Barrier Functions
This work addresses safety verification for autonomous systems in critical applications like human-robot interaction and autonomous driving, representing an incremental advancement in formal testing methods.
The paper tackles the problem of testing safety-critical autonomous systems by proposing a method to algorithmically generate test parameters based on control barrier functions, and demonstrates its effectiveness in identifying system failures in a case study using the Robotarium for waypoint navigation and obstacle avoidance.
The prolific rise in autonomous systems has led to questions regarding their safe instantiation in real-world scenarios. Failures in safety-critical contexts such as human-robot interactions or even autonomous driving can ultimately lead to loss of life. In this context, this paper aims to provide a method by which one can algorithmically test and evaluate an autonomous system. Given a black-box autonomous system with some operational specifications, we construct a minimax problem based on control barrier functions to generate a family of test parameters designed to optimally evaluate whether the system can satisfy the specifications. To illustrate our results, we utilize the Robotarium as a case study for an autonomous system that claims to satisfy waypoint navigation and obstacle avoidance simultaneously. We demonstrate that the proposed test synthesis framework systematically finds those sequences of events (tests) that identify points of system failure.