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Autonomous vehicles are in an intensive research and development stage, andthe organizations developing these systems are targeting to deploy them onpublic roads in a very near future. One of the expectations fromfully-automated vehicles is never to cause an accident. However, an automatedvehicle may not be able to avoid all collisions, e.g., the collisions caused byother road occupants. Hence, it is important for the system designers tounderstand the boundary case scenarios where an autonomous vehicle can nolonger avoid a collision. In this paper, an automated test generation approachthat utilizes Rapidly-exploring Random Trees is presented. A comparison of theproposed approach with an optimization-guided falsification approach from theliterature is provided. Furthermore, a cost function that guides the testgeneration toward almost-avoidable collisions or near-misses is proposed.

Rapidly-exploring Random Trees-based Test Generation for Autonomous Vehicles