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A comprehensive approach to evaluate human-machine conflicts in shared steering systems.

Shuguang Li1, Ling Deng1, Jierui Hu1

  • 1School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611730, China.

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Summary
This summary is machine-generated.

This study introduces a new, objective method to assess human-machine conflicts in collaborative driving systems. It uses eye-tracking, electromyography, and vehicle data to improve traffic safety and driving experience.

Keywords:
Automated drivingDriving simulationEvaluation methodHuman factorsHuman-machine collaborative drivingHuman-machine conflicts

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Area of Science:

  • Human-computer interaction
  • Automotive engineering
  • Traffic safety

Background:

  • Shared control in collaborative driving systems can cause human-machine conflicts, impacting safety and user experience.
  • Existing evaluation methods for these conflicts are subjective and lack comprehensive criteria, hindering objective assessment.

Purpose of the Study:

  • To develop a novel, objective, and efficient two-phase method for evaluating human-machine conflicts in collaborative driving.
  • To integrate diverse data sources for a more accurate assessment of driving experience and system performance.

Main Methods:

  • Driving simulation experiments were conducted to analyze correlations between subjective driving experience and objective indices.
  • Eye-tracking data, electromyography signals, and vehicle dynamic features were integrated using sparse principal component analysis (SPCA).
  • The proposed objective measure was validated against subjective driving experience data from post-drive questionnaires.

Main Results:

  • A strong correlation was found between subjective driving experience and selected objective indices.
  • The integrated objective measure demonstrated high effectiveness, with less than 7% error compared to subjective data.
  • The developed method provides a low-cost and high-efficiency approach to conflict evaluation.

Conclusions:

  • The proposed two-phase method offers a reliable and objective approach to evaluating human-machine conflicts in collaborative driving.
  • This contributes to the development of safer and more user-friendly collaborative driving systems.
  • The integration of physiological and vehicle data enhances the assessment of human-machine interaction.