Real-Time Lane Detection Based on dual attention mechanism transformer for ADAS/AD

Lane detection remains a high-priority research area for Advanced Driver Assistance Systems (ADAS) and Autonomous Driving (AD). To address the limitations of current mainstream convolutional neural networks in capturing global context and the elongated structure of lanes, we study a lane detection algorithm based on a dual attention mechanism using Transformer architecture (DATLD) for connected and autonomous vehicles. On the other hand, a new infrared sensor for onboard use that is not affected by light is used for lane line image acquisition to detect lane lines throughout the day. Firstly, an enhanced dense connected Backbone network is developed to improve channel feature extraction capability. Secondly, a novel Transformer architecture (CSVIT) based on a dual attention mechanism is designed to conduct global modelling from the two orthogonal dimensions of space and channel. Improve the accuracy of lane detection under different weather, environment and time conditions. Finally, the accuracy of DATLD on the TuSimple data set was improved to 95.71%, and the FPS was 1030, satisfying the lane detection real-time requirement of at least 30 FPS. The F1 score on the CULane dataset increased to 66.12%. We have verified the validity and feasibility of DATLD in the Autonomous-rail Rapid Transit (ART)Center, thus ensuring driving safety.