Efficient Computation of Invariably Safe States for Motion Planning of Self-Driving Vehicles

Abstract

Safe motion planning requires that a vehicle reaches a set of safe states at the end of the planning horizon. However, safe states of vehicles have not yet been systematically defined in the literature, nor does a computationally efficient way to obtain them for online motion planning exist. To tackle the aforementioned issues, we introduce invariably safe sets. These are regions that allow vehicles to remain safe for an infinite time horizon. We show how invariably safe sets can be computed and propose a tight under-approximation which can be obtained efficiently in linear time with respect to the number of traffic participants. We use invariably safe sets to lift safety verification from finite to infinite time horizons. In addition, our sets can be used to determine the existence of feasible evasive maneuvers and the criticality of scenarios by computing the time-to-react metric.