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Modeling distracted driving behavior considering cognitive processes.

Yixin Zhu1, Lishengsa Yue1, Qunli Zhang2

  • 1Department of Transportation Engineering, Tongji University, Key Laboratory of Road and Traffic Engineering, Ministry of Education, No. 4800, Cao'an road, Shanghai 201804, China.

Accident; Analysis and Prevention
|May 3, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a new model for distracted driving behavior using the queuing network model human processor framework. The model enhances accuracy by simulating cognitive processes, improving safety analysis for new vehicle technologies.

Keywords:
Cognitive processDistracted driving behaviorDistraction patternQueueing network model human processor

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Area of Science:

  • Cognitive Science
  • Traffic Safety Engineering
  • Human Factors in Transportation

Background:

  • Distracted driving behavior modeling remains incomplete, lacking comprehensive simulation of cognitive processes.
  • Existing models often focus on mathematical aspects of human factors, not the underlying brain information processing.

Purpose of the Study:

  • To propose a novel distracted driving model using the queuing network model human processor (QN-MHP) framework.
  • To simulate the cognitive processes of distracted drivers based on physiological and cognitive evidence.
  • To improve the accuracy and realism of distracted driving behavior models.

Main Methods:

  • Utilized the queuing network model human processor (QN-MHP) framework.
  • Incorporated a cumulative activation effect model for external stimuli and dual-task queuing/switching mechanisms for cognitive resource allocation.
  • Modeled driver actions using the Intelligent Driver Model (IDM) for both visual and auditory distractions.
  • Calibrated and verified the model using 773 distracted car-following events from the Shanghai Naturalistic Driving Study data.

Main Results:

  • The developed model demonstrated more uniform and reasonable parameter values.
  • Achieved significant accuracy improvements of 57% and 66% compared to two baseline models.
  • Successfully generated critical pre-crash scenarios and estimated distracted driving crash rates.

Conclusions:

  • The proposed QN-MHP based model offers a more realistic simulation of distracted driving by incorporating cognitive processes.
  • The model's enhanced accuracy and ability to predict critical scenarios contribute to traffic safety analysis and the development of new vehicle technologies.