用世界模型训练端到端自动驾驶策略，无需人工规则
Learning to Drive from a World Model
- 基于真实驾驶数据，在自建模拟器中训练端到端驾驶策略
- 两种仿真方法均能学出无规则依赖的驾驶行为
- 可在闭环仿真和实车系统中部署验证，适配真实场景
当前自动驾驶系统多依赖人工设计的感知输出与驾驶规则。本文提出一种端到端训练架构，直接利用真实驾驶数据，在在线策略模拟器中训练驾驶策略。通过两种仿真方式——重投影模拟与学习型世界模型，均实现了无需人工编码驾驶规则的策略训练。在闭环仿真及真实世界高级辅助驾驶系统中测试表明，该策略可有效学习驾驶行为，具有良好的可扩展性与落地潜力。
原文摘要 · Abstract (English)
Most self-driving systems rely on hand-coded perception outputs and engineered driving rules. Learning directly from human driving data with an end-to-end method can allow for a training architecture that is simpler and scales well with compute and data. In this work, we propose an end-to-end training architecture that uses real driving data to train a driving policy in an on-policy simulator. We show two different methods of simulation, one with reprojective simulation and one with a learned world model. We show that both methods can be used to train a policy that learns driving behavior without any hand-coded driving rules. We evaluate the performance of these policies in a closed-loop simulation and when deployed in a real-world advanced driver-assistance system.
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