Reliable and accurate lane detection has been a long-standing problem in the field of autonomous driving. In recent years, many approaches have been developed that use images (or videos) as input and reason in image space. In this paper we argue that accurate image estimates do not translate to precise 3D lane boundaries, which are the input required by modern motion planning algorithms. To address this issue, we propose a novel deep neural network that takes advantage of both LiDAR and camera sensors and produces very accurate estimates directly in 3D space. We demonstrate the performance of our approach on both highways and in cities, and show very accurate estimates in complex scenarios such as heavy traffic (which produces occlusion), fork, merges and intersections.
@article{arxiv.1905.01555,
title = {Deep Multi-Sensor Lane Detection},
author = {Min Bai and Gellert Mattyus and Namdar Homayounfar and Shenlong Wang and Shrinidhi Kowshika Lakshmikanth and Raquel Urtasun},
journal= {arXiv preprint arXiv:1905.01555},
year = {2019}
}
Comments
IEEE International Conference on Intelligent Robots and Systems (IROS) 2018