Lane Detection Model Based on Spatio-Temporal Network With Double Convolutional Gated Recurrent Units

TL;DR

This paper introduces a novel spatio-temporal network with double ConvGRUs for lane detection, effectively handling challenging scenes and outperforming existing models on large datasets.

Contribution

The paper proposes a new spatio-temporal network architecture with double ConvGRUs tailored for robust lane detection in complex driving scenarios.

Findings

Outperforms state-of-the-art models on TuSimple dataset

Effective in challenging scenes with occlusions and curves

Utilizes dual ConvGRUs for low-level feature extraction and temporal information processing

Abstract

Lane detection is one of the indispensable and key elements of self-driving environmental perception. Many lane detection models have been proposed, solving lane detection under challenging conditions, including intersection merging and splitting, curves, boundaries, occlusions and combinations of scene types. Nevertheless, lane detection will remain an open problem for some time to come. The ability to cope well with those challenging scenes impacts greatly the applications of lane detection on advanced driver assistance systems (ADASs). In this paper, a spatio-temporal network with double Convolutional Gated Recurrent Units (ConvGRUs) is proposed to address lane detection in challenging scenes. Both of ConvGRUs have the same structures, but different locations and functions in our network. One is used to extract the information of the most likely low-level features of lane markings. The extracted features are input into the next layer of the end-to-end network after concatenating them with the outputs of some blocks. The other one takes some continuous frames as its input to process the spatio-temporal driving information. Extensive experiments on the large-scale TuSimple lane marking challenge dataset and Unsupervised LLAMAS dataset demonstrate that the proposed model can effectively detect lanes in the challenging driving scenes. Our model can outperform the state-of-the-art lane detection models.