Modeling driver lane changing control with the queuing network-model human processor

Lu Zheng Bi; Jun Xing Shang; Guo Dong Gan

doi:10.1109/ICMLC.2012.6359460

Modeling driver lane changing control with the queuing network-model human processor

Lu Zheng Bi^*, Jun Xing Shang, Guo Dong Gan

^*Corresponding author for this work

School of Mechanical Engineering

Beijing Institute of Technology

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

3 Citations (Scopus)

Abstract

Computational models of driving behavior developed in a cognitive architecture can provide better scientific understanding of driving, simulate driving behavior, quantitatively predict possible interference of in-vehicle tasks, and thus help develop human factors guidelines and tools for in-vehicle systems design. Driver lane changing is a common activity in driving. Therefore, modeling driver lane changing control with a cognitive architecture should be an important component of cognitive models of driving behavior. In this paper, we develop a computational model of driver lane changing control with the Queuing Network-Model Human Processor (QN-MHP) cognitive architecture based on neuroscience and psychological findings. The simulation and experimental results from lane changing on straight and curved roads show that this model can perform the control process of lane changing well and the model's control process is consistent with that of drivers.

Original language	English
Title of host publication	Proceedings of 2012 International Conference on Machine Learning and Cybernetics, ICMLC 2012
Pages	830-834
Number of pages	5
DOIs	https://doi.org/10.1109/ICMLC.2012.6359460
Publication status	Published - 2012
Event	2012 International Conference on Machine Learning and Cybernetics, ICMLC 2012 - Xian, Shaanxi, China Duration: 15 Jul 2012 → 17 Jul 2012

Publication series

Name	Proceedings - International Conference on Machine Learning and Cybernetics
Volume	3
ISSN (Print)	2160-133X
ISSN (Electronic)	2160-1348

Conference

Conference	2012 International Conference on Machine Learning and Cybernetics, ICMLC 2012
Country/Territory	China
City	Xian, Shaanxi
Period	15/07/12 → 17/07/12

Keywords

Cognitive architecture
Computational model
Driver lane changing control
QN-MHP

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10.1109/ICMLC.2012.6359460

Cite this

Bi, L. Z., Shang, J. X., & Gan, G. D. (2012). Modeling driver lane changing control with the queuing network-model human processor. In Proceedings of 2012 International Conference on Machine Learning and Cybernetics, ICMLC 2012 (pp. 830-834). Article 6359460 (Proceedings - International Conference on Machine Learning and Cybernetics; Vol. 3). https://doi.org/10.1109/ICMLC.2012.6359460

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title = "Modeling driver lane changing control with the queuing network-model human processor",

abstract = "Computational models of driving behavior developed in a cognitive architecture can provide better scientific understanding of driving, simulate driving behavior, quantitatively predict possible interference of in-vehicle tasks, and thus help develop human factors guidelines and tools for in-vehicle systems design. Driver lane changing is a common activity in driving. Therefore, modeling driver lane changing control with a cognitive architecture should be an important component of cognitive models of driving behavior. In this paper, we develop a computational model of driver lane changing control with the Queuing Network-Model Human Processor (QN-MHP) cognitive architecture based on neuroscience and psychological findings. The simulation and experimental results from lane changing on straight and curved roads show that this model can perform the control process of lane changing well and the model's control process is consistent with that of drivers.",

keywords = "Cognitive architecture, Computational model, Driver lane changing control, QN-MHP",

author = "Bi, {Lu Zheng} and Shang, {Jun Xing} and Gan, {Guo Dong}",

year = "2012",

doi = "10.1109/ICMLC.2012.6359460",

language = "English",

isbn = "9781467314855",

series = "Proceedings - International Conference on Machine Learning and Cybernetics",

pages = "830--834",

booktitle = "Proceedings of 2012 International Conference on Machine Learning and Cybernetics, ICMLC 2012",

note = "2012 International Conference on Machine Learning and Cybernetics, ICMLC 2012 ; Conference date: 15-07-2012 Through 17-07-2012",

}

Bi, LZ, Shang, JX & Gan, GD 2012, Modeling driver lane changing control with the queuing network-model human processor. in Proceedings of 2012 International Conference on Machine Learning and Cybernetics, ICMLC 2012., 6359460, Proceedings - International Conference on Machine Learning and Cybernetics, vol. 3, pp. 830-834, 2012 International Conference on Machine Learning and Cybernetics, ICMLC 2012, Xian, Shaanxi, China, 15/07/12. https://doi.org/10.1109/ICMLC.2012.6359460

Modeling driver lane changing control with the queuing network-model human processor. / Bi, Lu Zheng; Shang, Jun Xing; Gan, Guo Dong.
Proceedings of 2012 International Conference on Machine Learning and Cybernetics, ICMLC 2012. 2012. p. 830-834 6359460 (Proceedings - International Conference on Machine Learning and Cybernetics; Vol. 3).

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

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AB - Computational models of driving behavior developed in a cognitive architecture can provide better scientific understanding of driving, simulate driving behavior, quantitatively predict possible interference of in-vehicle tasks, and thus help develop human factors guidelines and tools for in-vehicle systems design. Driver lane changing is a common activity in driving. Therefore, modeling driver lane changing control with a cognitive architecture should be an important component of cognitive models of driving behavior. In this paper, we develop a computational model of driver lane changing control with the Queuing Network-Model Human Processor (QN-MHP) cognitive architecture based on neuroscience and psychological findings. The simulation and experimental results from lane changing on straight and curved roads show that this model can perform the control process of lane changing well and the model's control process is consistent with that of drivers.

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Modeling driver lane changing control with the queuing network-model human processor

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