Modeling distracted driving behavior considering cognitive processes.

Autor: Zhu Y; Department of Transportation Engineering, Tongji University, Key Laboratory of Road and Traffic Engineering, Ministry of Education, No. 4800, Cao'an road, Shanghai 201804, China. Electronic address: zhuyixin@tongji.edu.cn., Yue L; Department of Transportation Engineering, Tongji University, Key Laboratory of Road and Traffic Engineering, Ministry of Education, No. 4800, Cao'an road, Shanghai 201804, China. Electronic address: 2014yuelishengsa@tongji.edu.cn., Zhang Q; HUAWEI Technologies Co. LTD, 2012 Lab, Huawei Headquarters Office Building, Bantian Street, Longgang District, Shenzhen 518129, China. Electronic address: zhangqunli1@huawei.com., Sun J; Department of Transportation Engineering, Tongji University, Key Laboratory of Road and Traffic Engineering, Ministry of Education, No. 4800, Cao'an road, Shanghai 201804, China. Electronic address: sunjian@tongji.edu.cn.
Jazyk: angličtina
Zdroj: Accident; analysis and prevention [Accid Anal Prev] 2024 Jul; Vol. 202, pp. 107602. Date of Electronic Publication: 2024 May 02.
DOI: 10.1016/j.aap.2024.107602
Abstrakt: The modeling of distracted driving behavior has been studied for many years, however, there remain many distraction phenomena that can not be fully modeled. This study proposes a new method that establishes the model using the queuing network model human processor (QN-MHP) framework. Unlike previous models that only consider distracted-driving-related human factors from a mathematical perspective, the proposed method reflects the information processing in the human brain, and simulates the distracted driver's cognitive processes based on a model structure supported by physiological and cognitive research evidence. Firstly, a cumulative activation effect model for external stimuli is adopted to mimic the phenomenon that a driver responds only to stimuli above a certain threshold. Then, dual-task queuing and switching mechanisms are modeled to reflect the cognitive resource allocation under distraction. Finally, the driver's action is modeled by the Intelligent Driver Model (IDM). The model is developed for visual distraction auditory distraction separately. 773 distracted car-following events from the Shanghai Naturalistic Driving Study data were used to calibrate and verify the model. Results show that the model parameters are more uniform and reasonable. Meanwhile, the model accuracy has improved by 57% and 66% compared to the two baseline models respectively. Moreover, the model demonstrates its ability to generate critical pre-crash scenarios and estimate the crash rate of distracted driving. The proposed model is expected to contribute to safety research regarding new vehicle technologies and traffic safety analysis.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024 Elsevier Ltd. All rights reserved.)
Databáze: MEDLINE
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