  The detection of small road hazards, such as lost cargo, is a vital
capability for self-driving cars. We tackle this challenging and rarely
addressed problem with a vision system that leverages appearance, contextual as
well as geometric cues. To utilize the appearance and contextual cues, we
propose a new deep learning-based obstacle detection framework. Here a variant
of a fully convolutional network is used to predict a pixel-wise semantic
labeling of (i) free-space, (ii) on-road unexpected obstacles, and (iii)
background. The geometric cues are exploited using a state-of-the-art detection
approach that predicts obstacles from stereo input images via model-based
statistical hypothesis tests. We present a principled Bayesian framework to
fuse the semantic and stereo-based detection results. The mid-level Stixel
representation is used to describe obstacles in a flexible, compact and robust
manner. We evaluate our new obstacle detection system on the Lost and Found
dataset, which includes very challenging scenes with obstacles of only 5 cm
height. Overall, we report a major improvement over the state-of-the-art, with
relative performance gains of up to 50%. In particular, we achieve a detection
rate of over 90% for distances of up to 50 m. Our system operates at 22 Hz on
our self-driving platform.
