@inproceedings{e9308326b37746be95c34b648ccac2f3,
title = "Application of Lane Detection Based on Point Instance Network in Autonomous Driving",
abstract = "Lane detection is the core problem of autonomous driving. After completing lane recognition, the autonomous driving system can realize the active safety and control function of vehicle lateral movement. However, the existing methods cannot adapt well to various environments and generate many unnecessary points, resulting in low detection accuracy. In this paper, Point Instance Network (PINet) based on key points estimation and instance segmentation is used, which is composed of several stacked hourglass networks that are trained at the same time. Compared with existing algorithms, PINet achieves ideal accuracy and false positive rate on CULane, especially in night and dazzle light.",
keywords = "Autonomous driving, Lane detection, Point Instance Network",
author = "Jialin Liu and Quanqing Yu and Pengyu Zhu",
note = "Publisher Copyright: {\textcopyright} 2023, Beijing Paike Culture Commu. Co., Ltd.; 5th International Conference on Energy Storage and Intelligent Vehicles, ICEIV 2022 ; Conference date: 03-12-2022 Through 04-12-2022",
year = "2023",
doi = "10.1007/978-981-99-1027-4\_106",
language = "英语",
isbn = "9789819910267",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "1014--1022",
editor = "Fengchun Sun and Qingxin Yang and Erik Dahlquist and Rui Xiong",
booktitle = "The Proceedings of the 5th International Conference on Energy Storage and Intelligent Vehicles, ICEIV 2022",
address = "德国",
}