ReachFlow: An Online Safety Assurance Framework for Waypoint-Following of Self-driving Cars

Abstract

Learning-enabled components have been widely deployed in autonomous systems. However, due to the weak interpretability and the prohibitively high complexity of large-scale machine learning models such as neural networks, reliability has been a crucial concern for safety-critical autonomous systems. This work proposes an online monitor called Reach-Flow for fault prevention of waypoint-following tasks for self-driving cars. It mainly consists of two components: (a) an online verification tool which conservatively checks the safety of the system behavior in the near future, and (b) a fallback controller which steers the system back to a desired state when the system is potentially unsafe. We implement ReachFlow in a self-driving racing car governed by a reinforcement learning-based controller. We demonstrate the effectiveness by rigorously verifying a safe waypoint-following control and providing a fallback control for an unsafe situation in which a large deviation from the planned path is predicted.