Rapidly-exploring Random Trees-based Test Generation for Autonomous Vehicles
This work addresses safety validation for autonomous vehicles, but it is incremental as it builds on existing test generation methods.
The paper tackles the problem of identifying boundary-case collision scenarios for autonomous vehicles by proposing an automated test generation approach using Rapidly-exploring Random Trees, and it compares this method with an existing optimization-guided falsification approach while introducing a cost function to guide tests toward near-miss scenarios.
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.