Related papers: Adaptive Failure Search Using Critical States from Domain Experts

Adaptive Failure Search Using Critical States from Domain Experts

URL: http://arxiv.org/abs/2304.00365v1
Date: Sat, 1 Apr 2023 18:14:41 GMT
Title: Adaptive Failure Search Using Critical States from Domain Experts
Authors: Peter Du, Katherine Driggs-Campbell
Abstract summary: Failure search may be done through logging substantial vehicle miles in either simulation or real world testing. AST is one such method that poses the problem of failure search as a Markov decision process. We show that the incorporation of critical states into the AST framework generates failure scenarios with increased safety violations.
Score: 9.93890332477992
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: 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 real world testing. Due to the sparsity of failure events, naive random search approaches require significant amounts of vehicle operation hours to find potential system weaknesses. As a result, adaptive searching techniques have been proposed to efficiently explore and uncover failure trajectories of an autonomous policy in simulation. Adaptive Stress Testing (AST) is one such method that poses the problem of failure search as a Markov decision process and uses reinforcement learning techniques to find high probability failures. However, this formulation requires a probability model for the actions of all agents in the environment. In systems where the environment actions are discrete and dependencies among agents exist, it may be infeasible to fully characterize the distribution or find a suitable proxy. This work proposes the use of a data driven approach to learn a suitable classifier that tries to model how humans identify {critical states and use this to guide failure search in AST. We show that the incorporation of critical states into the AST framework generates failure scenarios with increased safety violations in an autonomous driving policy with a discrete action space.