International Conferences
| Row-wise Lidar Lane Detection Network with Lane Correlation Refinement |
| Year |
2022 |
| Month |
|
| Journal |
2022 / IEEE Conference on Intelligent Transportation Systems (ITSC) |
| Author |
Dong-Hee Paek, Kevin Tirta Wijaya, Seung-Hyun Kong* |
|
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. |
https://ieeexplore.ieee.org/document/9922341
351회 연결
