Lane-DeepLab: Lane semantic segmentation in automatic driving scenarios for high-definition maps

Accurate high-definition maps with lane markings are often used as the navigation back-end for commercial autonomous vehicles. Currently, most high-definition maps are manually constructed by human labelling. Therefore, it is urgently required to propose a multi-class lane detection method that can automatically mark the road lanes to assist in generating high-precision maps for autonomous driving. We propose a lane segmentation detection method, named Lane-DeepLab, which is based on semantic segmentation for detecting multi-class lane lines in unmanned driving scenarios. The proposed method is based on the DeepLabv3+ network as the baseline, and we have redesigned the encoder-decoder structure to generate more accurate lane line detection results. More specifically, we restructure the atrous convolution at multi-scale by applying attention mechanism. Subsequently, we employ the Semantic Embedding Branch (SEB) to combine the high-level and low-level semantic information to obtain more abundant features, and use the Single Stage Headless (SSH) context module to obtain multi-scale information. Finally, we fuse the results to generate automatic high-precision mapping results. Our method has improved performance compared with other methods in the ApolloScape part of the dataset. Besides, in the database of Cityscapes, our approach has also achieved good results in semantic segmentation. Experimental results demonstrate that our proposed Lane-DeepLab can provide excellent performance in real traffic scenarios. (c) 2021 Elsevier B.V. All rights reserved.

Automated summary

A method named Lane-DeepLab is proposed for detecting multi-class lane lines in unmanned driving scenarios, which is based on semantic segmentation and redesigned the DeepLabv3+ network with attention mechanism for more accurate results. The method combines high-level and low-level semantic information to obtain abundant features and achieves automatic high-precision mapping results.