  Motion detection is a fundamental but challenging task for autonomous
driving. In particular scenes like highway, remote objects have to be paid
extra attention for better controlling decision. Aiming at distant vehicles, we
train a neural network model to classify the motion status using optical flow
field information as the input. The experiments result in high accuracy,
showing that our idea is viable and promising. The trained model also achieves
an acceptable performance for nearby vehicles. Our work is implemented in
PyTorch. Open tools including nuScenes, FastFlowNet and RAFT are used.
Visualization videos are available at
https://www.youtube.com/playlist?list=PLVVrWgq4OrlBnRebmkGZO1iDHEksMHKGk .
