LaneCorrect: Self-supervised Lane Detection

TL;DR

This paper introduces LaneCorrect, a self-supervised method for lane detection that leverages LiDAR data and geometric consistency to train models without human annotations, achieving competitive results across multiple benchmarks.

Contribution

The paper presents a novel self-supervised training scheme for lane detection that corrects labels automatically and transfers knowledge to arbitrary target datasets without human labels.

Findings

Achieves competitive performance on major lane detection benchmarks.

Effectively reduces domain gap between different datasets.

Demonstrates the feasibility of unsupervised lane detection using LiDAR data.

Abstract

Lane detection has evolved highly functional autonomous driving system to understand driving scenes even under complex environments. In this paper, we work towards developing a generalized computer vision system able to detect lanes without using any annotation. We make the following contributions: (i) We illustrate how to perform unsupervised 3D lane segmentation by leveraging the distinctive intensity of lanes on the LiDAR point cloud frames, and then obtain the noisy lane labels in the 2D plane by projecting the 3D points; (ii) We propose a novel self-supervised training scheme, dubbed LaneCorrect, that automatically corrects the lane label by learning geometric consistency and instance awareness from the adversarial augmentations; (iii) With the self-supervised pre-trained model, we distill to train a student network for arbitrary target lane (e.g., TuSimple) detection without any human labels; (iv) We thoroughly evaluate our self-supervised method on four major lane detection benchmarks (including TuSimple, CULane, CurveLanes and LLAMAS) and demonstrate excellent performance compared with existing supervised counterpart, whilst showing more effective results on alleviating the domain gap, i.e., training on CULane and test on TuSimple.