  With the rapidly growing interest in autonomous navigation, the body of
research on motion planning and collision avoidance techniques has enjoyed an
accelerating rate of novel proposals and developments. However, the complexity
of new techniques and their safety requirements render the bulk of current
benchmarking frameworks inadequate, thus leaving the need for efficient
comparison techniques unanswered. This work proposes a novel framework based on
deep reinforcement learning for benchmarking the behavior of collision
avoidance mechanisms under the worst-case scenario of dealing with an optimal
adversarial agent, trained to drive the system into unsafe states. We describe
the architecture and flow of this framework as a benchmarking solution, and
demonstrate its efficacy via a practical case study of comparing the
reliability of two collision avoidance mechanisms in response to intentional
collision attempts.
