Real-Time Category-Based and General Obstacle Detection for Autonomous Driving

Noa Garnett, Shai Silberstein, Shaul Oron, Ethan Fetaya, Uri Verner, Ariel Ayash, Vlad Goldner, Rafi Cohen, Kobi Horn, Dan Levi; Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017, pp. 198-205

Detecting obstacles, both dynamic and static, with near-to-perfect accuracy and low latency, is a crucial enabler of autonomous driving. In recent years obstacle detection methods increasingly rely on cameras instead of Lidars. Camera-based obstacle detection is commonly solved by detecting instances of known categories. However, in many situations the vehicle faces un-categorized obstacles, both static and dynamic. Column-based general obstacle detection covers all 3D obstacles but does not provide object classification, segmentation and motion prediction. In this paper we present a unified deep convolutional network combining these two complementary functions in one computationally efficient framework capable of real-time performance. In addition, we show several improvements to existing column-based obstacle detection, namely an improved network architecture, a new dataset and a major enhancement of the automatic ground truth algorithm.