@inproceedings{f8a9de485c2c4a239a7a9db3027cba53,
title = "A Multi-sensor Information Fusion Method for Autonomous Vehicle Perception System",
abstract = "Within the context of the environmental perception of autonomous vehicles (AVs), this paper establishes a sensor model based on the experimental sensor fusion of lidar and monocular cameras. The sensor fusion algorithm can map three-dimensional space coordinate points to a two-dimensional plane based on both space synchronization and time synchronization. The YOLO target recognition and density clustering algorithms obtain the data fusion containing the obstacles{\textquoteright} visual information and depth information. Furthermore, the experimental results show the high accuracy of the proposed sensor data fusion algorithm.",
keywords = "Autonomous driving, Lidar and Monocular camera, Sensor data fusion",
author = "Peng Mei and Karimi, \{Hamid Reza\} and Fei Ma and Shichun Yang and Cong Huang",
note = "Publisher Copyright: {\textcopyright} 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.; 7th EAI International Conference on Science and Technologies for Smart Cities, SmartCity360° 2021 ; Conference date: 02-12-2021 Through 04-12-2021",
year = "2022",
doi = "10.1007/978-3-031-06371-8\_40",
language = "英语",
isbn = "9783031063701",
series = "Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "633--646",
editor = "Sara Paiva and Xuejun Li and Lopes, \{S{\'e}rgio Ivan\} and Nishu Gupta and Rawat, \{Danda B.\} and Asma Patel and Karimi, \{Hamid Reza\}",
booktitle = "Science and Technologies for Smart Cities - 7th EAI International Conference, SmartCity360°, 2021, Proceedings",
address = "德国",
}