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

Understand

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.