Adaptive Stress Testing for Autonomous Vehicles
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Autonomous VehiclesDecision MakingDeep Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)Similar Papers 제목 키워드 기반
A novel framework for adaptive stress testing of autonomous vehicles in highways
Guaranteeing the safe operations of autonomous vehicles (AVs) is crucial for their widespread adoption and public acceptance. It is thus of a great significance to not only assess the AV against the standard safety tests…
Autonomous VehiclesDeep Reinforcement LearningFinding Failures in High-Fidelity Simulation using Adaptive Stress Testing and the Backward Algorithm
Validating the safety of autonomous systems generally requires the use of high-fidelity simulators that adequately capture the variability of real-world scenarios. However, it is generally not feasible to exhaustively se…
Autonomous VehiclesDeep Reinforcement Learningreinforcement-learningReinforcement Learning (RL)Adaptive Stress Testing with Reward Augmentation for Autonomous Vehicle Validation
Determining possible failure scenarios is a critical step in the evaluation of autonomous vehicle systems. Real-world vehicle testing is commonly employed for autonomous vehicle validation, but the costs and time require…
Reinforcement LearningAdaptive Failure Search Using Critical States from Domain Experts
Uncovering potential failure cases is a crucial step in the validation of safety critical systems such as autonomous vehicles. Failure search may be done through logging substantial vehicle miles in either simulation or …
Autonomous DrivingAutonomous VehiclesAdaptive Stress Testing: Finding Likely Failure Events with Reinforcement Learning
Finding the most likely path to a set of failure states is important to the analysis of safety-critical systems that operate over a sequence of time steps, such as aircraft collision avoidance systems and autonomous cars…
Autonomous DrivingCollision Avoidancereinforcement-learningReinforcement Learning+1