Our goal is to train a policy for autonomous driving via imitation learning
that is robust enough to drive a real vehicle. We find that standard behavior
cloning is insufficient for handling complex driving scenarios, even when we
leverage a perception system for preprocessing the input and a controller for
executing the output on the car: 30 million examples are still not enough. We
propose exposing the learner to synthesized data in the form of perturbations
to the expert's driving, which creates interesting situations such as
collisions and/or going off the road. Rather than purely imitating all data, we
augment the imitation loss with additional losses that penalize undesirable
events and encourage progress -- the perturbations then provide an important
signal for these losses and lead to robustness of the learned model. We show
that the ChauffeurNet model can handle complex situations in simulation, and
present ablation experiments that emphasize the importance of each of our
proposed changes and show that the model is responding to the appropriate
causal factors. Finally, we demonstrate the model driving a car in the real
world.

本研究旨在通过模仿学习训练一个自动驾驶策略，以使其足够强大以驾驶真实汽车。为了应对复杂的驾驶场景，我们建议在专家的驾驶基础上引入扰动来合成数据，并使用额外的损失函数强化模型。实验证明这种模型能够在模拟环境中适应复杂的驾驶场景，并能够在真实环境下驾驶汽车。