Analysis of driver's characteristics based on internet of vehicles

Jian Qun Wang; Xiao Qing Xue; Ning Cao

doi:10.4028/www.scientific.net/AMM.505-506.1148

Analysis of driver's characteristics based on internet of vehicles

Jian Qun Wang
, Xiao Qing Xue
, Ning Cao

School of Mechanical Engineering

Beijing Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

The road traffic accidents caused huge economic losses and casualties, so it had been focused by the researchers. Lane changing characteristic is the most relevant characteristic with safety. The intent of lane changing was discussed. Firstly, the factors affecting the intent were analyzed, the speed satisfaction value and the space satisfaction value were proposed; then the data from the University of California, Berkeley was extracted and the number of vehicles changed lane more often and the vehicle ID were obtained; the BP neural network classification model was established, it was trained and testified by actual data. The results shown the method could predict the intent accurately.

Original language	English
Title of host publication	Advances in Transportation
Pages	1148-1152
Number of pages	5
DOIs	https://doi.org/10.4028/www.scientific.net/AMM.505-506.1148
Publication status	Published - 2014
Event	3rd International Conference on Civil Engineering and Transportation, ICCET 2013 - Kunming, China Duration: 14 Dec 2013 → 15 Dec 2013

Publication series

Name	Applied Mechanics and Materials
Volume	505-506
ISSN (Print)	1660-9336
ISSN (Electronic)	1662-7482

Conference

Conference	3rd International Conference on Civil Engineering and Transportation, ICCET 2013
Country/Territory	China
City	Kunming
Period	14/12/13 → 15/12/13

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Classification model
Intent of lane changing
Space satisfaction value
Speed satisfaction value

Access to Document

10.4028/www.scientific.net/AMM.505-506.1148

Cite this

@inproceedings{5210c259efc24f7a9c32ecc883445dc9,

title = "Analysis of driver's characteristics based on internet of vehicles",

abstract = "The road traffic accidents caused huge economic losses and casualties, so it had been focused by the researchers. Lane changing characteristic is the most relevant characteristic with safety. The intent of lane changing was discussed. Firstly, the factors affecting the intent were analyzed, the speed satisfaction value and the space satisfaction value were proposed; then the data from the University of California, Berkeley was extracted and the number of vehicles changed lane more often and the vehicle ID were obtained; the BP neural network classification model was established, it was trained and testified by actual data. The results shown the method could predict the intent accurately.",

keywords = "Classification model, Intent of lane changing, Space satisfaction value, Speed satisfaction value",

author = "Wang, \{Jian Qun\} and Xue, \{Xiao Qing\} and Ning Cao",

year = "2014",

doi = "10.4028/www.scientific.net/AMM.505-506.1148",

language = "English",

isbn = "9783038350064",

series = "Applied Mechanics and Materials",

pages = "1148--1152",

booktitle = "Advances in Transportation",

note = "3rd International Conference on Civil Engineering and Transportation, ICCET 2013 ; Conference date: 14-12-2013 Through 15-12-2013",

}

TY - GEN

T1 - Analysis of driver's characteristics based on internet of vehicles

AU - Wang, Jian Qun

AU - Xue, Xiao Qing

AU - Cao, Ning

PY - 2014

Y1 - 2014

N2 - The road traffic accidents caused huge economic losses and casualties, so it had been focused by the researchers. Lane changing characteristic is the most relevant characteristic with safety. The intent of lane changing was discussed. Firstly, the factors affecting the intent were analyzed, the speed satisfaction value and the space satisfaction value were proposed; then the data from the University of California, Berkeley was extracted and the number of vehicles changed lane more often and the vehicle ID were obtained; the BP neural network classification model was established, it was trained and testified by actual data. The results shown the method could predict the intent accurately.

AB - The road traffic accidents caused huge economic losses and casualties, so it had been focused by the researchers. Lane changing characteristic is the most relevant characteristic with safety. The intent of lane changing was discussed. Firstly, the factors affecting the intent were analyzed, the speed satisfaction value and the space satisfaction value were proposed; then the data from the University of California, Berkeley was extracted and the number of vehicles changed lane more often and the vehicle ID were obtained; the BP neural network classification model was established, it was trained and testified by actual data. The results shown the method could predict the intent accurately.

KW - Classification model

KW - Intent of lane changing

KW - Space satisfaction value

KW - Speed satisfaction value

UR - https://www.scopus.com/pages/publications/84893908794

U2 - 10.4028/www.scientific.net/AMM.505-506.1148

DO - 10.4028/www.scientific.net/AMM.505-506.1148

M3 - Conference contribution

AN - SCOPUS:84893908794

SN - 9783038350064

T3 - Applied Mechanics and Materials

SP - 1148

EP - 1152

BT - Advances in Transportation

T2 - 3rd International Conference on Civil Engineering and Transportation, ICCET 2013

Y2 - 14 December 2013 through 15 December 2013

ER -

Analysis of driver's characteristics based on internet of vehicles

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

Other files and links

Fingerprint

Cite this