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.

深度学习在图像分类和目标检测领域取得了显着进展。然而，在自动驾驶研究中，复杂交通场景中的实时车道线检测和车道偏移估计一直是具有挑战性和困难的任务。传统的检测方法需要手动调整参数，面临诸多问题和困难，仍然极易受到物体遮挡、光照变化、路面磨损等干扰。设计稳健的车道检测和车道偏移估计算法仍然具有挑战性。在本文中，我们提出了一种用于复杂道路环境中车道偏移估计和车道线检测的卷积神经网络，它将车道线检测的问题转化为实例的分割。为了响应车道处理方法的变化，网络将对每条线路形成其示例。全局尺度感知优化机制旨在解决该问题，尤其是车道线宽度在车道消失点处逐渐变窄的情况。同时，为了实现多任务处理和提高性能，除了车道线检测网络之外，还使用了端到端的车道偏移估计网络。