通过优化真实驾驶场景生成安全关键场景，提升自动驾驶训练稳定性。
ReGentS: Real-World Safety-Critical Driving Scenario Generation Made Stable
- 基于可微分仿真器，对真实场景轨迹进行优化生成新场景。
- 生成的轨迹稳定且避免碰撞，解决传统方法发散与无效问题。
- 支持32个交通参与者，适合复杂真实路况训练需求。
基于机器学习的自动驾驶系统常面临现实中罕见的安全关键场景，阻碍其大规模部署。尽管增加真实数据覆盖可缓解此问题，但成本高且存在风险。本文通过轨迹优化方式，对复杂的现实常规场景进行修改，以生成安全关键驾驶场景。提出ReGentS框架，通过引入启发式规则稳定生成轨迹，避免明显碰撞和优化失败。该方法解决了传统生成中轨迹发散、不可避免碰撞等无用场景问题。同时，框架扩展至支持最多32个交通参与者的现实数据。利用可微分仿真器，简化了基于梯度下降的仿真优化过程，为未来研究奠定基础。代码已开源。
原文摘要 · Abstract (English)
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.
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