Lane Detection Transformer Based on Multi-frame Horizontal and Vertical Attention and Visual Transformer Module

Han Zhang, Yunchao Gu, Xinliang Wang, Junjun Pan, Minghui Wang

Abstract: Lane detection requires adequate global information due to the simplicity of lane line features and changeable road scenes. In this paper, we propose a novel lane detection Transformer based on multi-frame input to regress the parameters of lanes under a lane shape modeling. We design a Multi-frame Horizontal and Vertical Attention (MHVA) module to obtain more global features and use Visual Transformer (VT) module to get “lane tokens” with interaction information of lane instances. Extensive experiments on two public datasets show that our model can achieve state-of-art results on VIL-100 dataset and comparable performance on TuSimple dataset. In addition, our model runs at 46 fps on multi-frame data while using few parameters, indicating the feasibility and practicability in real-time self-driving applications of our proposed method.