Rapidly-Exploring Random Trees for Testing Automated Vehicles

Abstract

One of the expectations from fully or partially automated vehicles is to never cause an accident and actively avoid dangerous situations. However, an automated vehicle may not be able to avoid all collisions, e.g., collisions caused by other vehicles. Hence, it is important for the system developers to understand the boundary case scenarios where an autonomous vehicle can no longer avoid a collision. In this paper, an automated test generation approach that utilizes Rapidly-exploring Random Trees is presented to explore these boundary scenarios. An important advantage of the approach is the openness of the test scenarios: one can set the road geometry and the number of adversarial objects and let the system search for interesting trajectories and environment parameters. A cost function is proposed which guides the test generation toward almost-avoidable collisions or near-misses.