  Road detection is a critically important task for self-driving cars. By
employing LiDAR data, recent works have significantly improved the accuracy of
road detection. Relying on LiDAR sensors limits the wide application of those
methods when only cameras are available. In this paper, we propose a novel road
detection approach with RGB being the only input during inference.
Specifically, we exploit pseudo-LiDAR using depth estimation, and propose a
feature fusion network where RGB and learned depth information are fused for
improved road detection. To further optimize the network structure and improve
the efficiency of the network. we search for the network structure of the
feature fusion module using NAS techniques. Finally, be aware of that
generating pseudo-LiDAR from RGB via depth estimation introduces extra
computational costs and relies on depth estimation networks, we design a
modality distillation strategy and leverage it to further free our network from
these extra computational cost and dependencies during inference. The proposed
method achieves state-of-the-art performance on two challenging benchmarks,
KITTI and R2D.
