Row-wise LiDAR Lane Detection Network with Lane Correlation Refinement

• URL: http://arxiv.org/abs/2210.08745v1
• Date: Mon, 17 Oct 2022 04:47:08 GMT
• Title: Row-wise LiDAR Lane Detection Network with Lane Correlation Refinement
• Authors: Dong-Hee Paek, Kevin Tirta Wijaya, Seung-Hyun Kong
• Abstract summary: We propose a novel two-stage LiDAR lane detection network with row-wise detection approach. The first-stage network produces lane proposals through a global feature correlator backbone and a row-wise detection head. The proposed network advances the state-of-the-art in terms of F1-score with 30% less GFLOPs.
• Score: 1.6832237384792461
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Lane detection is one of the most important functions for autonomous driving. In recent years, deep learning-based lane detection networks with RGB camera images have shown promising performance. However, camera-based methods are inherently vulnerable to adverse lighting conditions such as poor or dazzling lighting. Unlike camera, LiDAR sensor is robust to the lighting conditions. In this work, we propose a novel two-stage LiDAR lane detection network with row-wise detection approach. The first-stage network produces lane proposals through a global feature correlator backbone and a row-wise detection head. Meanwhile, the second-stage network refines the feature map of the first-stage network via attention-based mechanism between the local features around the lane proposals, and outputs a set of new lane proposals. Experimental results on the K-Lane dataset show that the proposed network advances the state-of-the-art in terms of F1-score with 30% less GFLOPs. In addition, the second-stage network is found to be especially robust to lane occlusions, thus, demonstrating the robustness of the proposed network for driving in crowded environments.

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