World Engine：迈向自动驾驶后训练时代
World Engine: Towards the Era of Post-Training for Autonomous Driving
AI总结：
提出World Engine生成式框架，通过从真实日志重建高保真交互环境并外推安全关键变体，利用强化后训练对齐策略与安全约束，显著减少罕见安全关键场景故障，提升自动驾驶安全性。
摘要：
自动驾驶车辆必须在现实世界中安全运行，而错误可能带来严重后果。尽管现代端到端驾驶策略在常规场景中表现出色，但其可靠性受限于真实驾驶数据集中安全关键的“长尾”事件的稀缺性。这些罕见交互定义了学习策略的实际安全边界，但在现实世界中难以大规模收集。我们展示了这一根本限制可以通过在合成的关键交互上对预训练驾驶模型进行后训练来解决。我们引入了World Engine，一个生成式框架，从真实日志中重建高保真交互环境，并系统性地将其外推为现实的安全关键变体。这一范式使得基于强化的后训练能够将策略与安全约束对齐，规避现实世界探索中固有的物理风险。在基于nuPlan构建的公开基准上，World Engine显著减少了罕见安全关键场景中的故障，并且相比仅扩展预训练数据带来了更大的增益。此外，当部署到生产级自动驾驶系统时，所得策略减少了模拟碰撞，并在道路测试中显示出可衡量的改进，表明在合成的安全关键交互上进行后训练为更安全的自动驾驶提供了一条可扩展且有效的途径。完整的代码库套件（包括训练）已向公众发布。
英文摘要：
Autonomous vehicles must operate safely in the real world, where errors can have severe consequences. Although modern end-to-end driving policies excel in routine scenarios, their reliability is limited by the scarcity of safety-critical ``long-tail'' events in real driving datasets. These rare interactions define the practical safety boundary of the learned policy, yet they are difficult to collect at scale in the real world. Here we show that this fundamental limitation can be addressed by post-training pre-trained driving models on synthesized high-stakes interactions. We introduce World Engine, a generative framework that reconstructs high-fidelity interactive environments from real-world logs and systematically extrapolates them into realistic safety-critical variations. This paradigm enables reinforcement-based post-training to align policies with safety constraints, circumventing the physical risks inherent in real-world exploration. On a public benchmark built on nuPlan, World Engine substantially reduces failures in rare safety-critical scenarios and yields significantly larger gains than scaling pre-training data alone. Furthermore, when deployed on a production-scale autonomous driving system, the resulting policy reduces simulated collisions and demonstrates measurable improvements in on-road testing, showing that post-training on synthesized, safety-critical interactions offers a scalable and effective pathway to safer autonomous driving. The full codebase suite, including training, is released to the public.
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