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Recently, deep-learning based approaches have achieved impressive performancefor autonomous driving. However, end-to-end vision-based methods typically havelimited interpretability, making the behaviors of the deep networks difficultto explain. Hence, their potential applications could be limited in practice.To address this problem, we propose an interpretable end-to-end vision-basedmotion planning approach for autonomous driving, referred to as IVMP. Given aset of past surrounding-view images, our IVMP first predicts future egocentricsemantic maps in bird's-eye-view space, which are then employed to plantrajectories for self-driving vehicles. The predicted future semantic maps notonly provide useful interpretable information, but also allow our motionplanning module to handle objects with low probability, thus improving thesafety of autonomous driving. Moreover, we also develop an optical flowdistillation paradigm, which can effectively enhance the network while stillmaintaining its real-time performance. Extensive experiments on the nuScenesdataset and closed-loop simulation show that our IVMP significantly outperformsthe state-of-the-art approaches in imitating human drivers with a much highersuccess rate. Our project page is available athttps://sites.google.com/view/ivmp.

Learning Interpretable End-to-End Vision-Based Motion Planning for Autonomous Driving with Optical Flow Distillation