Rapidly-exploring Random Trees-based Test Generation for Autonomous Vehicles
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Autonomous vehicles are in an intensive research and development stage, and the organizations developing these systems are targeting to deploy them on public roads in a very near future. One of the expectations from fully-automated vehicles is never to cause an accident. However, an automated vehicle may not be able to avoid all collisions, e.g., the collisions caused by other road occupants. Hence, it is important for the system designers 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. A comparison of the proposed approach with an optimization-guided falsification approach from the literature is provided. Furthermore, a cost function that guides the test generation toward almost-avoidable collisions or near-misses is proposed.
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