Title: CCIL: Context-conditioned imitation learning for urban driving. Abstract: Imitation learning is a promising solution to the challenging autonomous urban driving task as experienced human drivers can effortlessly tackle highly complex driving scenarios. Behavior cloning is the most widely applied imitation learning approach in autonomous driving due to its exemption from potentially risky online interactions, but it suffers from the covariate shift issue. To mitigate this problem, we propose a context-conditioned imitation learning approach that learns a policy to map the context state into the ego vehicle's state instead of the typical formulation from both ego and context state to the ego action.  Besides, to make full use of the spatial and temporal relations in the context to infer the ego future states, we design a novel policy network based on the Transformer, whose attention mechanism has demonstrated excellent performance in capturing relations. Finally, during evaluation, a linear quadratic controller is employed to produce smooth planning based on the predicted states from the policy network. Experiments on the real-world large-scale Lyft and nuPlan datasets demonstrate that our method can surpass the state-of-the-art method significantly.
