Adversarial Reinforcement Learning Framework for Benchmarking Collision Avoidance Mechanisms in Autonomous Vehicles
This work addresses the need for efficient comparison techniques for collision avoidance mechanisms in autonomous vehicles, which is crucial for safety but currently lacks robust benchmarking, though it is incremental as it builds on existing reinforcement learning methods.
The paper tackles the problem of inadequate benchmarking for collision avoidance mechanisms in autonomous vehicles by proposing a novel framework based on deep reinforcement learning that uses an adversarial agent to simulate worst-case scenarios, demonstrating its efficacy in a case study comparing two mechanisms' reliability against intentional collision attempts.
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.