TARConvGRU: A Cross-dimension Spatiotemporal Model for Lane Detection

Spatiotemporal information plays a critical role in the autonomous driving environment. In the task of lane detection, the features extracted by the existing spatiotemporal work from a video or consecutive frames contain a lot of irrelevant information, which reduces the performance of the model to a certain extent. To address the trouble, we proposed a novel lane detection model with the TARConvGRU module. This module can help our model focus on the feature representation and feature location of lane lines by channel and spatial operations and consciously guides some computing resources tend to the most likely lane line features by capturing the cross-dimension information interaction. Furthermore, we demonstrate the effectiveness of the proposed model on three popular lane detection benchmarks: TuSimple, Unsupervised Labeled Lane Markers (Unsupervised LLAMAS), and Dynamic Vision Sensor Dataset (DET). The experiments show that our model achieves competitive results compared with other state-of-the-art models. In addition, we discuss some significant problems encountered in the designing process in detail in the ablation study.