Fuzzy logic based merging gap acceptance model incorporating driving styles and drivers’ personalities

Chen Chai; Xuesong Wang; Yiik Diew Wong; Yidan Gao

doi:10.1007/978-3-319-60441-1_92

Fuzzy logic based merging gap acceptance model incorporating driving styles and drivers’ personalities

Chen Chai^*, Xuesong Wang, Yiik Diew Wong, Yidan Gao

^*Corresponding author for this work

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

Abstract

Cognitive ability and decision-making process in the driving task vary among drivers. Differences in driving styles and driver’s personality affect driving behavior, risk-taking and responses under hazardous scenarios. An innovative fuzzy logic based gap acceptance simulation model is proposed to extract and predict gap acceptance behavior with driving styles and drivers’ personalities. The proposed approach models driver’s decision-making process by a fuzzy set of the input factors. Cognitions, such as perceptions of front gap and current velocity, are modelled as fuzzy inputs while decisions, such as deceleration, are modelled as fuzzy outputs. For each driver, weighting factors are applied to differentiate driver’s sensitivity to operational factors, such as front gap and relative velocity during gap acceptance behavior.

Original language	English
Title of host publication	Advances in Human Aspects of Transportation - Proceedings of the AHFE 2017 International Conference on Human Factors in Transportation, 2017
Editors	Neville A. Stanton
Publisher	Springer Verlag
Pages	963-970
Number of pages	8
ISBN (Print)	9783319604404
DOIs	https://doi.org/10.1007/978-3-319-60441-1_92
Publication status	Published - 2018
Externally published	Yes
Event	AHFE 2017 International Conference on Human Factors in Transportation, 2017 - Los Angeles, United States Duration: Jul 17 2017 → Jul 21 2017

Publication series

Name	Advances in Intelligent Systems and Computing
Volume	597
ISSN (Print)	2194-5357

Conference

Conference	AHFE 2017 International Conference on Human Factors in Transportation, 2017
Country/Territory	United States
City	Los Angeles
Period	7/17/17 → 7/21/17

Bibliographical note

Publisher Copyright:
© Springer International Publishing AG 2018.

ASJC Scopus Subject Areas

Control and Systems Engineering
General Computer Science

Keywords

Driving styles
Fuzzy logic
Gap acceptance
Human factors
Merging behavior

Access to Document

10.1007/978-3-319-60441-1_92

Cite this

Chai, C., Wang, X., Wong, Y. D., & Gao, Y. (2018). Fuzzy logic based merging gap acceptance model incorporating driving styles and drivers’ personalities. In N. A. Stanton (Ed.), Advances in Human Aspects of Transportation - Proceedings of the AHFE 2017 International Conference on Human Factors in Transportation, 2017 (pp. 963-970). (Advances in Intelligent Systems and Computing; Vol. 597). Springer Verlag. https://doi.org/10.1007/978-3-319-60441-1_92

Chai, Chen ; Wang, Xuesong ; Wong, Yiik Diew et al. / Fuzzy logic based merging gap acceptance model incorporating driving styles and drivers’ personalities. Advances in Human Aspects of Transportation - Proceedings of the AHFE 2017 International Conference on Human Factors in Transportation, 2017. editor / Neville A. Stanton. Springer Verlag, 2018. pp. 963-970 (Advances in Intelligent Systems and Computing).

@inproceedings{cce5b32577f847c6a2f45b4d127b8627,

title = "Fuzzy logic based merging gap acceptance model incorporating driving styles and drivers{\textquoteright} personalities",

abstract = "Cognitive ability and decision-making process in the driving task vary among drivers. Differences in driving styles and driver{\textquoteright}s personality affect driving behavior, risk-taking and responses under hazardous scenarios. An innovative fuzzy logic based gap acceptance simulation model is proposed to extract and predict gap acceptance behavior with driving styles and drivers{\textquoteright} personalities. The proposed approach models driver{\textquoteright}s decision-making process by a fuzzy set of the input factors. Cognitions, such as perceptions of front gap and current velocity, are modelled as fuzzy inputs while decisions, such as deceleration, are modelled as fuzzy outputs. For each driver, weighting factors are applied to differentiate driver{\textquoteright}s sensitivity to operational factors, such as front gap and relative velocity during gap acceptance behavior.",

keywords = "Driving styles, Fuzzy logic, Gap acceptance, Human factors, Merging behavior",

author = "Chen Chai and Xuesong Wang and Wong, {Yiik Diew} and Yidan Gao",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2018.; AHFE 2017 International Conference on Human Factors in Transportation, 2017 ; Conference date: 17-07-2017 Through 21-07-2017",

year = "2018",

doi = "10.1007/978-3-319-60441-1_92",

language = "English",

isbn = "9783319604404",

series = "Advances in Intelligent Systems and Computing",

publisher = "Springer Verlag",

pages = "963--970",

editor = "Stanton, {Neville A.}",

booktitle = "Advances in Human Aspects of Transportation - Proceedings of the AHFE 2017 International Conference on Human Factors in Transportation, 2017",

address = "Germany",

}

Chai, C, Wang, X, Wong, YD & Gao, Y 2018, Fuzzy logic based merging gap acceptance model incorporating driving styles and drivers’ personalities. in NA Stanton (ed.), Advances in Human Aspects of Transportation - Proceedings of the AHFE 2017 International Conference on Human Factors in Transportation, 2017. Advances in Intelligent Systems and Computing, vol. 597, Springer Verlag, pp. 963-970, AHFE 2017 International Conference on Human Factors in Transportation, 2017, Los Angeles, United States, 7/17/17. https://doi.org/10.1007/978-3-319-60441-1_92

Fuzzy logic based merging gap acceptance model incorporating driving styles and drivers’ personalities. / Chai, Chen; Wang, Xuesong; Wong, Yiik Diew et al.
Advances in Human Aspects of Transportation - Proceedings of the AHFE 2017 International Conference on Human Factors in Transportation, 2017. ed. / Neville A. Stanton. Springer Verlag, 2018. p. 963-970 (Advances in Intelligent Systems and Computing; Vol. 597).

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

TY - GEN

T1 - Fuzzy logic based merging gap acceptance model incorporating driving styles and drivers’ personalities

AU - Chai, Chen

AU - Wang, Xuesong

AU - Wong, Yiik Diew

AU - Gao, Yidan

N1 - Publisher Copyright: © Springer International Publishing AG 2018.

PY - 2018

Y1 - 2018

N2 - Cognitive ability and decision-making process in the driving task vary among drivers. Differences in driving styles and driver’s personality affect driving behavior, risk-taking and responses under hazardous scenarios. An innovative fuzzy logic based gap acceptance simulation model is proposed to extract and predict gap acceptance behavior with driving styles and drivers’ personalities. The proposed approach models driver’s decision-making process by a fuzzy set of the input factors. Cognitions, such as perceptions of front gap and current velocity, are modelled as fuzzy inputs while decisions, such as deceleration, are modelled as fuzzy outputs. For each driver, weighting factors are applied to differentiate driver’s sensitivity to operational factors, such as front gap and relative velocity during gap acceptance behavior.

AB - Cognitive ability and decision-making process in the driving task vary among drivers. Differences in driving styles and driver’s personality affect driving behavior, risk-taking and responses under hazardous scenarios. An innovative fuzzy logic based gap acceptance simulation model is proposed to extract and predict gap acceptance behavior with driving styles and drivers’ personalities. The proposed approach models driver’s decision-making process by a fuzzy set of the input factors. Cognitions, such as perceptions of front gap and current velocity, are modelled as fuzzy inputs while decisions, such as deceleration, are modelled as fuzzy outputs. For each driver, weighting factors are applied to differentiate driver’s sensitivity to operational factors, such as front gap and relative velocity during gap acceptance behavior.

KW - Driving styles

KW - Fuzzy logic

KW - Gap acceptance

KW - Human factors

KW - Merging behavior

UR - http://www.scopus.com/inward/record.url?scp=85022342917&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85022342917&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-60441-1_92

DO - 10.1007/978-3-319-60441-1_92

M3 - Conference contribution

AN - SCOPUS:85022342917

SN - 9783319604404

T3 - Advances in Intelligent Systems and Computing

SP - 963

EP - 970

BT - Advances in Human Aspects of Transportation - Proceedings of the AHFE 2017 International Conference on Human Factors in Transportation, 2017

A2 - Stanton, Neville A.

PB - Springer Verlag

T2 - AHFE 2017 International Conference on Human Factors in Transportation, 2017

Y2 - 17 July 2017 through 21 July 2017

ER -

Chai C, Wang X, Wong YD, Gao Y. Fuzzy logic based merging gap acceptance model incorporating driving styles and drivers’ personalities. In Stanton NA, editor, Advances in Human Aspects of Transportation - Proceedings of the AHFE 2017 International Conference on Human Factors in Transportation, 2017. Springer Verlag. 2018. p. 963-970. (Advances in Intelligent Systems and Computing). doi: 10.1007/978-3-319-60441-1_92

Fuzzy logic based merging gap acceptance model incorporating driving styles and drivers’ personalities

Abstract

Publication series

Conference

Bibliographical note

ASJC Scopus Subject Areas

Keywords

Access to Document

Other files and links

Fingerprint

Cite this