@conference {Ramos2017,
	title = {Detecting unexpected obstacles for self-driving cars: Fusing deep learning and geometric modeling},
	booktitle = {IEEE Intelligent Vehicles Symposium, Proceedings},
	year = {2017},
	month = {dec},
	pages = {1025{\textendash}1032},
	abstract = {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 proposed 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 a performance gain of 27.4\%. 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.},
	isbn = {9781509048045},
	doi = {10.1109/IVS.2017.7995849},
	url = {http://arxiv.org/abs/1612.06573},
	author = {Ramos, Sebastian and Gehrig, Stefan and Pinggera, Peter and Franke, Uwe and Carsten Rother}
}
