@inproceedings{2f90240c1e9740208603bee4301c8d05,
title = "Radar and Camera Fusion based Moving Obstacle Tracking for Automated Vehicles",
abstract = "In this paper, a multi-sensor fusion based environment perception architecture for ground unmanned vehicles is proposed. The target-level multi-sensor fusion technology is presented to take advantages of camera and millimeter wave (MMW) radar in target perception. On this basis, a multi-target tracking model is designed to solve the problems of alignment, association, uncertainty, as well as the elimination of false data. In order to verify the stability and real-time performance of the proposed algorithm, a real vehicle test was implemented according to the statistical data and relevant indicators. The results show that the proposed algorithm can effectively perceive and track multiple obstacles in real scene.",
keywords = "Camera, Environmental perception, Multi-sensor fusion, Radar",
author = "Shihao Wang and Zheng Ma and Ying Li and Chao Yang and Weida Wang and Changle Xiang",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 5th CAA International Conference on Vehicular Control and Intelligence, CVCI 2021 ; Conference date: 29-10-2021 Through 31-10-2021",
year = "2021",
doi = "10.1109/CVCI54083.2021.9661136",
language = "English",
series = "2021 5th CAA International Conference on Vehicular Control and Intelligence, CVCI 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2021 5th CAA International Conference on Vehicular Control and Intelligence, CVCI 2021",
address = "United States",
}