@inproceedings{b184939c01d144379df902c3b286b7fb,
title = "An Effective Method for Self-driving Car Navigation based on Lidar",
abstract = "Existing navigation methods are generally based on GPS or cameras and these methods have limitations in terms of signal strength and brightness. To overcome drawbacks of navigation methods above, we propose a Lidar-based Navigation Approach (LNA) to predict movement trajectory of self-driving vehicles through road edges information, and this approach is a fitting and real-time regression method. By combining regression model with vehicle coordinate system, navigation trajectory is accurately generated. Experiments on common road scenarios demonstrate that our approach is effective to improve navigation techniques.",
keywords = "LNA, Lidar, Linear regression, Navigation, Self-driving car",
author = "Meng Liu and Yu Liu and Jianwei Niu and Yu Du and Yanchen Wan",
note = "Publisher Copyright: {\textcopyright} 2018, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.; 13th International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2017 ; Conference date: 11-12-2017 Through 13-12-2017",
year = "2018",
doi = "10.1007/978-3-030-00916-8\_65",
language = "英语",
isbn = "9783030009151",
series = "Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST",
publisher = "Springer Verlag",
pages = "718--727",
editor = "Imed Romdhani and Lei Shu and Timothy Gordon and Hara Takahiro and Zhangbing Zhou and Deze Zeng",
booktitle = "Collaborative Computing",
address = "德国",
}