Computational Modeling of Driving Behaviors: Challenges and Approaches

Myounghoon Jeon; Yiqi Zhang; Heejin Jeong; Christian Janssen; Shan Bao

doi:10.1145/3473682.3477436

Computational Modeling of Driving Behaviors: Challenges and Approaches

Myounghoon Jeon, Yiqi Zhang, Heejin Jeong, Christian Janssen, Shan Bao

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

Abstract

Computational modeling has great advantages in human behavior research, such as abstracting the problem space, simulating the situation by varying critical variables, and predicting future outcomes. Although much research has been conducted on driver behavior modeling, relatively little modeling research has appeared at the Auto-UI Conferences. If any, most work has focused on qualitative models about manual driving. In this workshop, we will first describe why computational driver behavior modeling is crucial for automotive research and then, introduce recent driver modeling research to researchers, practitioners, and students. By identifying research gaps and exploring solutions together, we expect to form the basis of a new modeling special interest group combining the Auto-UI community and the computational modeling community. The workshop will be closed with suggestions on the directions for future transdisciplinary work.

Original language	English
Title of host publication	AutomotiveUI '21 Adjunct
Subtitle of host publication	13th International Conference on Automotive User Interfaces and Interactive Vehicular Applications
Place of Publication	New York
Publisher	Association for Computing Machinery
Pages	160-163
Number of pages	4
ISBN (Electronic)	9781450386418
DOIs	https://doi.org/10.1145/3473682.3477436
Publication status	Published - 9 Sept 2021
Event	13th ACM International Conference on Automotive User Interfaces and Interactive Vehicular Applications, AutomotiveUI 2021 - Virtual, Online, United Kingdom Duration: 9 Sept 2021 → 14 Sept 2021

Conference

Conference	13th ACM International Conference on Automotive User Interfaces and Interactive Vehicular Applications, AutomotiveUI 2021
Country/Territory	United Kingdom
City	Virtual, Online
Period	9/09/21 → 14/09/21

Bibliographical note

DBLP License: DBLP's bibliographic metadata records provided through http://dblp.org/ are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.

Keywords

automated driving
computational modeling
driver behavior modeling

Access to Document

10.1145/3473682.3477436

3473682.3477436Final published version, 405 KBLicence: Taverne

Cite this

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title = "Computational Modeling of Driving Behaviors: Challenges and Approaches",

abstract = "Computational modeling has great advantages in human behavior research, such as abstracting the problem space, simulating the situation by varying critical variables, and predicting future outcomes. Although much research has been conducted on driver behavior modeling, relatively little modeling research has appeared at the Auto-UI Conferences. If any, most work has focused on qualitative models about manual driving. In this workshop, we will first describe why computational driver behavior modeling is crucial for automotive research and then, introduce recent driver modeling research to researchers, practitioners, and students. By identifying research gaps and exploring solutions together, we expect to form the basis of a new modeling special interest group combining the Auto-UI community and the computational modeling community. The workshop will be closed with suggestions on the directions for future transdisciplinary work.",

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author = "Myounghoon Jeon and Yiqi Zhang and Heejin Jeong and Christian Janssen and Shan Bao",

note = "DBLP License: DBLP's bibliographic metadata records provided through http://dblp.org/ are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.; 13th ACM International Conference on Automotive User Interfaces and Interactive Vehicular Applications, AutomotiveUI 2021 ; Conference date: 09-09-2021 Through 14-09-2021",

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Jeon, M, Zhang, Y, Jeong, H, Janssen, C & Bao, S 2021, Computational Modeling of Driving Behaviors: Challenges and Approaches. in AutomotiveUI '21 Adjunct: 13th International Conference on Automotive User Interfaces and Interactive Vehicular Applications. Association for Computing Machinery, New York, pp. 160-163, 13th ACM International Conference on Automotive User Interfaces and Interactive Vehicular Applications, AutomotiveUI 2021, Virtual, Online, United Kingdom, 9/09/21. https://doi.org/10.1145/3473682.3477436

Computational Modeling of Driving Behaviors: Challenges and Approaches. / Jeon, Myounghoon; Zhang, Yiqi; Jeong, Heejin et al.
AutomotiveUI '21 Adjunct: 13th International Conference on Automotive User Interfaces and Interactive Vehicular Applications. New York: Association for Computing Machinery, 2021. p. 160-163.

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

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AU - Bao, Shan

N1 - DBLP License: DBLP's bibliographic metadata records provided through http://dblp.org/ are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.

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N2 - Computational modeling has great advantages in human behavior research, such as abstracting the problem space, simulating the situation by varying critical variables, and predicting future outcomes. Although much research has been conducted on driver behavior modeling, relatively little modeling research has appeared at the Auto-UI Conferences. If any, most work has focused on qualitative models about manual driving. In this workshop, we will first describe why computational driver behavior modeling is crucial for automotive research and then, introduce recent driver modeling research to researchers, practitioners, and students. By identifying research gaps and exploring solutions together, we expect to form the basis of a new modeling special interest group combining the Auto-UI community and the computational modeling community. The workshop will be closed with suggestions on the directions for future transdisciplinary work.

AB - Computational modeling has great advantages in human behavior research, such as abstracting the problem space, simulating the situation by varying critical variables, and predicting future outcomes. Although much research has been conducted on driver behavior modeling, relatively little modeling research has appeared at the Auto-UI Conferences. If any, most work has focused on qualitative models about manual driving. In this workshop, we will first describe why computational driver behavior modeling is crucial for automotive research and then, introduce recent driver modeling research to researchers, practitioners, and students. By identifying research gaps and exploring solutions together, we expect to form the basis of a new modeling special interest group combining the Auto-UI community and the computational modeling community. The workshop will be closed with suggestions on the directions for future transdisciplinary work.

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Computational Modeling of Driving Behaviors: Challenges and Approaches

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Conference

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Keywords

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