Multi-scale spatial convolution algorithm for lane line detection and lane offset estimation in complex road conditions

Abstract

Deep learning has made remarkable progress in the field of image classification and object detection. Nevertheless, in the autonomous driving research, the real-time lane line detection and lane offset estimation in complex traffic scenes have always been challenging and difficult tasks. Traditional detection methods need manual adjustment of parameters, they face many problems and difficulties and are still highly susceptible to interference caused by obstructing objects, illumination changes, and pavement wear. It is still challenging to design a robust lane detection and lane offset estimation algorithm. In this paper, we propose a convolutional neural network for lane offset estimation and lane line detection in a complex road environment, which transforms the problems of lane line detection into the instance’s segmentation. In response to a change in the method of lane processing, the network will form its example to each line. The global scale perception optimization mechanism is designed to solve the issue, especially where the lane line width is gradually narrowing at the vanishing point of the lane. At the same time, to realize multi-tasking processing and improve performance, and end-to-end lane offset estimation network is used in addition to the lane line detection network.