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

Understand

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.