DQFORMER: Dynamic Query Transformer for Lane Detection

Hao Yang, Shuyuan Lin, Runqing Jiang, Yang Lu, Hanzi Wang

Abstract: Lane detection is one of the most important tasks in self-driving. The critical purpose of lane detection is the prediction of lane shapes. Meanwhile, it is challenging and difficult to determine lane instance positions before predicting lane shapes in an image. In this paper, we propose a top-down method called Dynamic Query Transformer (DQFormer), which uses a Dynamic Lane Queries (DLQs) module to predict lane shapes. Specifically, to accurately predict lane shapes, we propose a new framework for generating dynamic weights based on DLQs, which can focus on the context of lane shapes dynamically. Unlike existing transformer-based methods, the proposed DQFormer does not require setting a fixed number of lane queries, so it is suitable for various scenes. In addition, we further propose a Line Voting Module (LVM) which collects votes from other lanes to enhance lane features, to determine lane instance positions. Extensive experiments demonstrate that DQFormer outperforms several state-of-the-art methods on two popular lane detection benchmarks (i.e., CULane and TuSimple).