REVIEW ARTICLE
Warning of Potential Collision for Vehicles
Huang Yue*, Qin Gui He, Liu Tong, Sun Ning, Wang Xiao Dan
College of Computer Science and Technology/Center for Computer Fundamental Education, Jilin University,
Changchun, China
Article Information
Identifiers and Pagination:
Year: 2015Volume: 9
First Page: 806
Last Page: 811
Publisher Id: TOMEJ-9-806
DOI: 10.2174/1874155X01509010806
Article History:
Received Date: 26/05/2015Revision Received Date: 14/07/2015
Acceptance Date: 10/08/2015
Electronic publication date: 7/10/2015
Collection year: 2015
© 2015 Yue et al
open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: (https://creativecommons.org/licenses/by/4.0/legalcode). This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: (https://creativecommons.org/licenses/by/4.0/legalcode). This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Abstract
A moving vehicle may very likely run into accidents. The occurrence rate of accidents would be largely reduced if the driver is warned in advance, even only 0.5 s earlier. For a running vehicle, the driving route within short time before collision has the characteristic of Markov. In this case, the coordinates of position only have to be considered within a short range, rather than the running status during the past long period. Within short period before collision, the driving route can be basically divided into two states: a straight line and a binomial curve. In this paper, a mechanism is proposed for sending collision warning messages to running vehicles.
Keywords: Curve fitting, collision, dissemination, emergency warning, VANET.