Learning to Collide: An Adaptive Safety-Critical Scenarios Generating Method

• URL: http://arxiv.org/abs/2003.01197v3
• Date: Thu, 23 Jul 2020 02:08:13 GMT
• Title: Learning to Collide: An Adaptive Safety-Critical Scenarios Generating Method
• Authors: Wenhao Ding, Baiming Chen, Minjun Xu, Ding Zhao
• Abstract summary: We propose a generative framework to create safety-critical scenarios for evaluating task algorithms. We demonstrate that the proposed framework generates safety-critical scenarios more efficiently than grid search or human design methods.
• Score: 20.280573307366627
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Long-tail and rare event problems become crucial when autonomous driving algorithms are applied in the real world. For the purpose of evaluating systems in challenging settings, we propose a generative framework to create safety-critical scenarios for evaluating specific task algorithms. We first represent the traffic scenarios with a series of autoregressive building blocks and generate diverse scenarios by sampling from the joint distribution of these blocks. We then train the generative model as an agent (or a generator) to investigate the risky distribution parameters for a given driving algorithm being evaluated. We regard the task algorithm as an environment (or a discriminator) that returns a reward to the agent when a risky scenario is generated. Through the experiments conducted on several scenarios in the simulation, we demonstrate that the proposed framework generates safety-critical scenarios more efficiently than grid search or human design methods. Another advantage of this method is its adaptiveness to the routes and parameters.

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