<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Ramos, Sebastian</style></author><author><style face="normal" font="default" size="100%">Gehrig, Stefan</style></author><author><style face="normal" font="default" size="100%">Pinggera, Peter</style></author><author><style face="normal" font="default" size="100%">Franke, Uwe</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Detecting unexpected obstacles for self-driving cars: Fusing deep learning and geometric modeling</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE Intelligent Vehicles Symposium, Proceedings</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year><pub-dates><date><style  face="normal" font="default" size="100%">dec</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://arxiv.org/abs/1612.06573</style></url></web-urls></urls><pages><style face="normal" font="default" size="100%">1025–1032</style></pages><isbn><style face="normal" font="default" size="100%">9781509048045</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">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.</style></abstract></record></records></xml>