Deep Multi-Sensor Lane Detection
This addresses the need for precise 3D lane boundaries in autonomous driving motion planning, offering a domain-specific improvement over image-based methods.
The paper tackles the problem of inaccurate 3D lane boundary estimation from images for autonomous driving by proposing a deep neural network that uses LiDAR and camera sensors to produce accurate 3D estimates directly, demonstrating high accuracy in complex scenarios like heavy traffic and intersections.
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.