TL;DR
This paper introduces K-Lane, the first large public lidar lane dataset for urban and highway roads, and proposes a new lidar lane detection network that outperforms existing camera-based methods especially under challenging lighting and occlusion conditions.
Contribution
The paper provides the K-Lane dataset with extensive annotations and introduces LLDN-GFC, a novel lidar lane detection network utilizing global feature correlation for improved robustness.
Findings
LLDN-GFC achieves 82.1% F1-score on K-Lane.
LLDN-GFC outperforms camera-based lane detection under poor lighting.
The K-Lane dataset contains over 15,000 frames with diverse conditions.
Abstract
Lane detection is a critical function for autonomous driving. With the recent development of deep learning and the publication of camera lane datasets and benchmarks, camera lane detection networks (CLDNs) have been remarkably developed. Unfortunately, CLDNs rely on camera images which are often distorted near the vanishing line and prone to poor lighting condition. This is in contrast with Lidar lane detection networks (LLDNs), which can directly extract the lane lines on the bird's eye view (BEV) for motion planning and operate robustly under various lighting conditions. However, LLDNs have not been actively studied, mostly due to the absence of large public lidar lane datasets. In this paper, we introduce KAIST-Lane (K-Lane), the world's first and the largest public urban road and highway lane dataset for Lidar. K-Lane has more than 15K frames and contains annotations of up to six…
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