The geometric attention-aware network for lane detection in complex road scenes.

Jianwu Long,Lang Peng,Tong Li,Zeran Yan,Feng Chen

doi:10.1371/journal.pone.0254521

Jianwu Long, Lang Peng + Show 3 more

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https://doi.org/10.1371/journal.pone.0254521

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Abstract

Lane detection in complex road scenes is still a challenging task due to poor lighting conditions, interference of irrelevant road markings or signs, etc. To solve the problem of lane detection in the various complex road scenes, we proposed a geometric attention-aware network (GAAN) for lane detection. The proposed GAAN adopted a multi-task branch architecture, and used the attention information propagation (AIP) module to perform communication between branches, then the geometric attention-aware (GAA) module was used to complete feature fusion. In order to verify the lane detection effect of the proposed model in this paper, the experiments were conducted on the CULane dataset, TuSimple dataset, and BDD100K dataset. The experimental results show that our method performs well compared with the current excellent lane line detection networks.

Highlights

Lane detection is a basic but still challenging task [1,2,3,4,5] in perceptions of autonomous vehicle, which requires that algorithm can detect the lane lines from traffic scene image captured by car cameras
In order to verify the effectiveness of the geometric attention-aware network (GAAN) in lane detection of complex road scenes, experiments were conducted on the TuSimple dataset, the CULane dataset, and the BDD100K dataset
The TuSimple dataset focuses on highway scenes, the CULane dataset and the BDD100K dataset mainly focus on urban road scenes

Summary

Introduction

Lane detection is a basic but still challenging task [1,2,3,4,5] in perceptions of autonomous vehicle, which requires that algorithm can detect the lane lines from traffic scene image captured by car cameras. Segmented lane lines are available for trajectory tracking control and positioning vehicles in autonomous driving, detected lanes can be used to judge the status of other traffic participants. It is a pivotal part of making highly precision maps and crashing prediction [9,10,11]. Considerably blocked cars would cover the lane lines, which makes fully convolution network tends to predict discontinuous or fuzzy lane lines These situations bring great challenges to lane detection methods based on semantic segmentation

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