ReGentS: Real-World Safety-Critical Driving Scenario Generation Made Stable
This work addresses the challenge of rare safety-critical scenarios in autonomous driving training, which is incremental by building on existing trajectory optimization methods with stabilization and heuristic improvements.
The paper tackles the problem of generating safety-critical driving scenarios for autonomous vehicle training by modifying real-world regular scenarios through trajectory optimization, resulting in a stable method that handles up to 32 agents and avoids unrealistic collisions.
Machine learning based autonomous driving systems often face challenges with safety-critical scenarios that are rare in real-world data, hindering their large-scale deployment. While increasing real-world training data coverage could address this issue, it is costly and dangerous. This work explores generating safety-critical driving scenarios by modifying complex real-world regular scenarios through trajectory optimization. We propose ReGentS, which stabilizes generated trajectories and introduces heuristics to avoid obvious collisions and optimization problems. Our approach addresses unrealistic diverging trajectories and unavoidable collision scenarios that are not useful for training robust planner. We also extend the scenario generation framework to handle real-world data with up to 32 agents. Additionally, by using a differentiable simulator, our approach simplifies gradient descent-based optimization involving a simulator, paving the way for future advancements. The code is available at https://github.com/valeoai/ReGentS.