Adaptive Stress Testing for Autonomous Vehicles
This addresses safety testing for autonomous vehicles, offering a scalable approach that could improve reliability, though it is incremental as it builds on existing reinforcement learning techniques.
The paper tackles the problem of testing autonomous vehicle decision systems by developing a method that uses reinforcement learning to find likely collision scenarios, showing that deep reinforcement learning finds more probable failures than Monte Carlo tree search with fewer simulator calls.
This paper presents a method for testing the decision making systems of autonomous vehicles. Our approach involves perturbing stochastic elements in the vehicle's environment until the vehicle is involved in a collision. Instead of applying direct Monte Carlo sampling to find collision scenarios, we formulate the problem as a Markov decision process and use reinforcement learning algorithms to find the most likely failure scenarios. This paper presents Monte Carlo Tree Search (MCTS) and Deep Reinforcement Learning (DRL) solutions that can scale to large environments. We show that DRL can find more likely failure scenarios than MCTS with fewer calls to the simulator. A simulation scenario involving a vehicle approaching a crosswalk is used to validate the framework. Our proposed approach is very general and can be easily applied to other scenarios given the appropriate models of the vehicle and the environment.