  Inter-vehicle distance and relative velocity estimations are two basic
functions for any ADAS (Advanced driver-assistance systems). In this paper, we
propose a monocular camera-based inter-vehicle distance and relative velocity
estimation method based on end-to-end training of a deep neural network. The
key novelty of our method is the integration of multiple visual clues provided
by any two time-consecutive monocular frames, which include deep feature clue,
scene geometry clue, as well as temporal optical flow clue. We also propose a
vehicle-centric sampling mechanism to alleviate the effect of perspective
distortion in the motion field (i.e. optical flow). We implement the method by
a light-weight deep neural network. Extensive experiments are conducted which
confirm the superior performance of our method over other state-of-the-art
methods, in terms of estimation accuracy, computational speed, and memory
footprint.

Computer Vision and Pattern Recognition;Machine Learning;Robotics