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.

利用外观、语境和几何线索，通过一个基于深度学习的完全卷积网络，结合基于模型的统计假设检验的最新检测方法，和一个系统的贝叶斯框架来检测自动驾驶汽车所面临的道路中的障碍物。我们在失物招领数据集上评估了我们的障碍物检测系统，取得了 50% 的相对性能提高和在 50 米内超过 90% 的检测率，在我们的自动驾驶平台上达到了 22 Hz 的操作。