Simultaneously searching and solving multiple avoidable collisions for testing autonomous driving systems
Alessandro Calò, Paolo Arcaini, Shaukat Ali, Florian Hauer, Fuyuki Ishikawa
Abstract
The oracle problem is a key issue in testing Autonomous Driving Systems (ADS): when a collision is found, it is not always clear whether the ADS is responsible for it. Our recent search-based testing approach offers a solution to this problem by defining a collision as avoidable if a differently configured ADS would have avoided it. This approach searches for both collision scenarios and the ADS configurations capable of avoiding them. However, its main problem is that the ADS configurations generated for avoiding some collisions are not suitable for preventing other ones. Therefore, it does not provide any guidance to automotive engineers for improving the safety of the ADS. To this end, we propose a new search-based approach to generate configurations of the ADS that can avoid as many different types of collisions as possible. We present two versions of the approach, which differ in the way of searching for collisions and alternative configurations. The approaches have been experimented on the path planner component of an ADS provided by our industry partner.