  With the recent advancement of deep learning technology, data-driven
approaches for autonomous car prediction and planning have achieved
extraordinary performance. Nevertheless, most of these approaches follow a
non-interactive prediction and planning paradigm, hypothesizing that a
vehicle's behaviors do not affect others. The approaches based on such a
non-interactive philosophy typically perform acceptably in sparse traffic
scenarios but can easily fail in dense traffic scenarios. Therefore, we propose
an end-to-end interactive neural motion planner (INMP) for autonomous driving
in this paper. Given a set of past surrounding-view images and a high
definition map, our INMP first generates a feature map in bird's-eye-view
space, which is then processed to detect other agents and perform interactive
prediction and planning jointly. Also, we adopt an optical flow distillation
paradigm, which can effectively improve the network performance while still
maintaining its real-time inference speed. Extensive experiments on the
nuScenes dataset and in the closed-loop Carla simulation environment
demonstrate the effectiveness and efficiency of our INMP for the detection,
prediction, and planning tasks. Our project page is at
sites.google.com/view/inmp-ofd.
