@inproceedings{bce432d7e6014e56a657d679a2f7b983,
title = "Uncertainty Estimation of Location Information under Vehicle-Vehicle Cooperative Control",
abstract = "With the rapid development of vehicle intelligence and communication technology, vehicle-vehicle cooperative technology plays an increasingly important role in intelligent transportation systems. The core of vehicle-vehicle cooperative technology is the vehicle movement state data collection and interaction. In traditional vehicle data processing, GPS data is mostly filtered, and the output is a single set value. In this study, an extended Kalman filter was used to filter out the uncertainty of vehicle's motion state data, except for a single determinant, and the approximate distribution of the data was obtained. Through these distributions, the Monte Carlo method is used to calculate the safety evaluation indexes including time to collision, time headway, and safety margin. Fitting the evaluation index with the common distribution, the results show that the safety evaluation index is subject to the Burr distribution.",
keywords = "Kalman filter, Monte Carlo, Vehicle-vehicle cooperative, uncertainty",
author = "Junda Zhai and Guangquan Lu",
note = "Publisher Copyright: {\textcopyright} 2018 American Society of Civil Engineers.; 18th COTA International Conference of Transportation Professionals: Intelligence, Connectivity, and Mobility, CICTP 2018 ; Conference date: 05-07-2018 Through 08-07-2018",
year = "2018",
doi = "10.1061/9780784481523.007",
language = "英语",
series = "CICTP 2018: Intelligence, Connectivity, and Mobility - Proceedings of the 18th COTA International Conference of Transportation Professionals",
publisher = "American Society of Civil Engineers (ASCE)",
pages = "66--75",
editor = "Xiaokun Wang and Yu Zhang and Diange Yang and Zheng You",
booktitle = "CICTP 2018",
address = "美国",
}