D-3DLD: Depth-Aware Voxel Space Mapping for Monocular 3D Lane Detection with Uncertainty

Abstract

The estimation of 3D lanes from monocular RGB images is a fundamentally ill-posed problem. Previous studies have assumed that all lanes are on a flat ground plane. However, we argue that the algorithms based on this assumption have difficulty in detecting various lanes in actual driving environments. Contrary to previous approaches, we expand rich contextual features from an image domain to a 3D space by utilizing depth-aware voxel mapping. In addition, we determine 3D lanes based on voxelized features. We design a new lane representation combined with uncertainties and predict the confidence intervals of 3D lane points using Laplace loss. Experimental results show that the proposed method achieves state-of-the-art detection accuracy on three challenging datasets, including two real-world datasets, and significantly outperforms existing methods with reasonable computation load.