  This paper documents the winning entry at the CVPR2017 vehicle velocity
estimation challenge. Velocity estimation is an emerging task in autonomous
driving which has not yet been thoroughly explored. The goal is to estimate the
relative velocity of a specific vehicle from a sequence of images. In this
paper, we present a light-weight approach for directly regressing vehicle
velocities from their trajectories using a multilayer perceptron. Another
contribution is an explorative study of features for monocular vehicle velocity
estimation. We find that light-weight trajectory based features outperform
depth and motion cues extracted from deep ConvNets, especially for far-distance
predictions where current disparity and optical flow estimators are challenged
significantly. Our light-weight approach is real-time capable on a single CPU
and outperforms all competing entries in the velocity estimation challenge. On
the test set, we report an average error of 1.12 m/s which is comparable to a
(ground-truth) system that combines LiDAR and radar techniques to achieve an
error of around 0.71 m/s.
