Lane Detection Model Based on Spatio-Temporal Network With Double Convolutional Gated Recurrent Units
This addresses a key problem for self-driving cars and ADAS by improving detection in complex scenarios, though it appears incremental as it builds on existing ConvGRU methods.
The paper tackles lane detection in challenging driving scenes like intersections and occlusions by proposing a spatio-temporal network with double Convolutional Gated Recurrent Units, achieving state-of-the-art performance on the TuSimple and LLAMAS datasets.
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.