Pseudo-LiDAR-Based Road Detection

• Published in: IEEE transactions on circuits and systems for video technology Vol. 32; no. 8; pp. 5386 - 5398
• Main Authors: Sun, Libo, Zhang, Haokui, Yin, Wei
• Format: Journal Article
• Language: English
• Published: New York IEEE 01-08-2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Autonomous cars; Color imagery; Computer architecture; Distillation; drivable area detection; Image segmentation; Knowledge engineering; Laser radar; Lidar; Road detection; Roads; Semantics; Sensors; Task analysis
• ISSN: 1051-8215; 1558-2205
• DOI: 10.1109/TCSVT.2022.3146305

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. However, relying on LiDAR sensors limits the application of those methods when only cameras are available. In this paper, we propose a novel road detection approach with RGB images being the only input. Specifically, we exploit pseudo-LiDAR using depth estimation and propose a feature fusion network in which RGB images and learned depth information are fused for improved road detection. To optimize the network architecture and improve the efficiency of our network, we propose a method to search for the information propagation paths. Finally, to reduce the computational cost, we design a modality distillation strategy to avoid using depth estimation networks during inference. The resulting model eliminates the reliance on LiDAR sensors and achieves state-of-the-art performance on two challenging benchmarks, KITTI and R2D.