  Recently, deep-learning based approaches have achieved impressive performance
for autonomous driving. However, end-to-end vision-based methods typically have
limited interpretability, making the behaviors of the deep networks difficult
to explain. Hence, their potential applications could be limited in practice.
To address this problem, we propose an interpretable end-to-end vision-based
motion planning approach for autonomous driving, referred to as IVMP. Given a
set of past surrounding-view images, our IVMP first predicts future egocentric
semantic maps in bird's-eye-view space, which are then employed to plan
trajectories for self-driving vehicles. The predicted future semantic maps not
only provide useful interpretable information, but also allow our motion
planning module to handle objects with low probability, thus improving the
safety of autonomous driving. Moreover, we also develop an optical flow
distillation paradigm, which can effectively enhance the network while still
maintaining its real-time performance. Extensive experiments on the nuScenes
dataset and closed-loop simulation show that our IVMP significantly outperforms
the state-of-the-art approaches in imitating human drivers with a much higher
success rate. Our project page is available at
https://sites.google.com/view/ivmp.
