CenterLineDet: CenterLine Graph Detection for Road Lanes with Vehicle-mounted Sensors by Transformer for HD Map Generation
Zhenhua Xu, Yuxuan Liu, Yuxiang Sun, Ming Liu, Lujia Wang

TL;DR
CenterLineDet is a transformer-based method that automatically detects lane centerlines from vehicle-mounted sensors, enabling efficient HD map generation for autonomous driving by handling complex topologies like intersections.
Contribution
This paper introduces CenterLineDet, a novel imitation learning-based transformer approach for automatic lane centerline detection in HD maps, addressing complex topologies and overlapping issues.
Findings
Outperforms existing methods on NuScenes dataset
Effectively detects complex lane topologies including intersections
Demonstrates robustness with vehicle-mounted sensor data
Abstract
With the fast development of autonomous driving technologies, there is an increasing demand for high-definition (HD) maps, which provide reliable and robust prior information about the static part of the traffic environments. As one of the important elements in HD maps, road lane centerline is critical for downstream tasks, such as prediction and planning. Manually annotating centerlines for road lanes in HD maps is labor-intensive, expensive and inefficient, severely restricting the wide applications of autonomous driving systems. Previous work seldom explores the lane centerline detection problem due to the complicated topology and severe overlapping issues of lane centerlines. In this paper, we propose a novel method named CenterLineDet to detect lane centerlines for automatic HD map generation. Our CenterLineDet is trained by imitation learning and can effectively detect the graph…
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Taxonomy
TopicsAutonomous Vehicle Technology and Safety · Advanced Neural Network Applications · Video Surveillance and Tracking Methods
