Developing autonomous vehicles that can navigate complex environments with human-level safety and efficiency is a central goal in self-driving research. A common approach to achieving this is imitation learning, where agents are trained to mimic human expert demonstrations collected from real-world driving scenarios. However, discrepancies between human perception and the self-driving car's sensors can introduce an \textit{imitation gap}, leading to imitation learning failures. In this work, we introduce \textbf{IGDrivSim}, a benchmark built on top of the Waymax simulator, designed to investigate the effects of the imitation gap in learning autonomous driving policy from human expert demonstrations. Our experiments show that this perception gap between human experts and self-driving agents can hinder the learning of safe and effective driving behaviors. We further show that combining imitation with reinforcement learning, using a simple penalty reward for prohibited behaviors, effectively mitigates these failures. Our code is open-sourced at: https://github.com/clemgris/IGDrivSim.git.

本研究针对自主驾驶领域中的模仿学习所面临的“模仿差距”问题进行探讨，该差距源于人类感知与无人驾驶汽车传感器的差异。我们提出了IGDrivSim基准，以研究这种模仿差距对自主驾驶政策学习的影响，并发现结合模仿学习与强化学习的方法能够有效降低这种学习失败的概率，从而提升安全有效的驾驶行为。

IGDrivSim：自主驾驶模仿差距的基准