Accurate Lane Detection with Atrous Convolution and Spatial Pyramid Pooling for Autonomous Driving

Engineering notes

However, the current state-of-the-art lane detection accuracy is still not satisfactory for realizing fully autonomous driving. The experimental results on the public Tusimple dataset show that our network outperforms the state-of-the-arts.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

Lane detection is a fundamental capability for autonomous driving. Many effective lane detection algorithms based on traditional computer vision and recent deep learning technologies have been proposed. However, the current state-of-the-art lane detection accuracy is still not satisfactory for realizing fully autonomous driving. Thus, this paper proposes a new lane detection network using atrous convolution and spatial pyramid pooling techniques to improve the lane detection accuracy. We address the detection problem with pixel-wise semantic segmentation. Our network consists of one encoder and two decoders, which outputs a binary segmentation map and an embedded feature map, respectively. The embedded feature map is employed for clustering algorithms to separate segmented lane pixels into different lanes. The experimental results on the public Tusimple dataset show that our network outperforms the state-of-the-arts.

Links and sources

• Official / arXiv page

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