关键词: 车道线检测；自动驾驶；神经网络；语义分割

Abstract: This article proposes a lane line detection and fitting method based on the results of semantic segmentation. The detection of ground landmarks such as lane lines is a significant content of the environment perception of autonomous vehicles, which can provide the vehicle with information about the driving area. The vehicle-mounted monocular camera obtains the road images collected during the driving process of the vehicle and sends them to the convolutional neural network for semantic segmentation of lane lines. The segmented binary image containing only lane lines is perspective transformed to obtain a bird's-eye view, the effective lane line pixels are screened, the effective lane line points are polynomial fitting using the least square method, and the left and right lane line polynomial fitting coefficients are output. It can effectively solve the problems of poor environmental adaptability of traditional lane line detection algorithms, weak robustness, and inaccurate detection information for curved lane lines.

Key words: Lane line detection; Autopilot; Neural networks; Semantic segmentation