K-Lane: Lidar Lane Dataset and Benchmark for Urban Roads and Highways

Paek, Dong-Hee; Kong, Seung-Hyung; Wijaya, Kevin Tirta

doi:10.1109/cvprw56347.2022.00491

Cited by 17 publications

(10 citation statements)

References 16 publications

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“…In this section, we provide the implementation details that are required to reproduce our results. Then we show and discuss our experimental results on the K-Lane dataset [9].…”

Section: Methodsmentioning

confidence: 99%

“…In [16], a CNN backbone is utilized to detect the ego lane lines in the highways. In [9], a segmentation-based neural network with global feature correlator backbone is used to perform LiDAR lane detection under various environments in the K-Lane dataset.…”

Section: Lidar Lane Detectionmentioning

confidence: 99%

“…This results in the final output feature map of size C head × H BEV ×W BEV . We utilize the same feature extractor as seen in previous state-of-the-art network [9] as our focus in this work is on the detection head with the two-stage row-wise formulation.…”

Section: B Feature Extractormentioning

confidence: 99%

“…We set the lane detection problem as a row-wise prediction task instead of a semantic segmentation task, so that the detection head of the network performs row-wise MLP operations instead of grid-wise MLP operations. The row-wise formulation leads to a significantly less computational cost than the prior work [9], with about 30% less GFLOPs.…”

Section: Introductionmentioning

confidence: 96%

“…Recently, [9] opens a large-scale LiDAR lane detection dataset, K-Lane, to the public, along with a segmentationbased LiDAR lane detection network (LLDN) baseline. The baseline consists of a projection network, a global feature correlator (GFC), and a segmentation head, which generates a feature map in the BEV format, extracting features via global correlation, and predicting lanes per grid, respectively.…”

Section: Introductionmentioning

confidence: 99%

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Row-wise LiDAR Lane Detection Network with Lane Correlation Refinement

Paek

Wijaya

Kong

2022

2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)

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Lane detection is one of the most important functions for autonomous driving. In recent years, deep learningbased 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 F1score 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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“…In this section, we provide the implementation details that are required to reproduce our results. Then we show and discuss our experimental results on the K-Lane dataset [9].…”

Section: Methodsmentioning

confidence: 99%

Section: Lidar Lane Detectionmentioning

confidence: 99%

Section: B Feature Extractormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

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