Generating Useful Accident-Prone Driving Scenarios via a Learned Traffic Prior

Understand

Evaluating and improving planning for autonomous vehicles requires scalable generation of long-tail traffic scenarios.

• To be useful, these scenarios must be realistic and challenging, but not impossible to drive through safely.
• In this work, we introduce STRIVE, a method to automatically generate challenging scenarios that cause a given planner to produce undesirable behavior, like collisions.
• To maintain scenario plausibility, the key idea is to leverage a learned model of traffic motion in the form of a graph-based conditional VAE.