Asymmetric Strip Transformer With Position Vectors Embedding for Lane Detection

作者：Jing Zhang, Yao Le, Shumeng Zhang, Yunsong Li · 发表于：IEEE Transactions on Intelligent Transportation Systems · 年份：2025 · DOI：10.1109/tits.2025.3630188 · 被引用次数：1 · 研究领域：Autonomous Vehicle Technology and Safety、Advanced Neural Network Applications、Automated Road and Building Extraction

Lane detection is an important aspect of autonomous driving environment perception. Traditionally, lane detection has been regarded as a semantic segmentation task, and the geometric characteristics and position information of lanes have been ignored. Different from previous models, we proposed a model to capture the high-level semantic features and low-level position features of lanes by adopting two modules in the row and column. In the horizontal direction, we utilized line shape self-attention to capture the long-distance dependencies of lanes, which is crucial due to the slender shape of lanes, while reducing unnecessary computational resources to obtain irrelevant features. We used position information vectors encoding in the Key, Query, and Value modules in the transformer to enable considering the position information to explore potential location associations between lane and employed it for the vertical direction. In the Tusimple benchmark test, this method achieved an accuracy rate of 96.74%, demonstrating good competitiveness compared with existing methods.