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The detection of small road hazards, such as lost cargo, is a vitalcapability for self-driving cars. We tackle this challenging and rarelyaddressed problem with a vision system that leverages appearance, contextual aswell as geometric cues. To utilize the appearance and contextual cues, wepropose a new deep learning-based obstacle detection framework. Here a variantof a fully convolutional network is used to predict a pixel-wise semanticlabeling of (i) free-space, (ii) on-road unexpected obstacles, and (iii)background. The geometric cues are exploited using a state-of-the-art detectionapproach that predicts obstacles from stereo input images via model-basedstatistical hypothesis tests. We present a principled Bayesian framework tofuse the semantic and stereo-based detection results. The mid-level Stixelrepresentation is used to describe obstacles in a flexible, compact and robustmanner. We evaluate our new obstacle detection system on the Lost and Founddataset, which includes very challenging scenes with obstacles of only 5 cmheight. Overall, we report a major improvement over the state-of-the-art, withrelative performance gains of up to 50%. In particular, we achieve a detectionrate of over 90% for distances of up to 50 m. Our system operates at 22 Hz onour self-driving platform.

Detecting Unexpected Obstacles for Self-Driving Cars: Fusing Deep Learning and Geometric Modeling