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Related Experiment Video

Updated: May 26, 2025

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A personalized human-machine shared driving system: A case study of obstacle avoidance.

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Summary

This study developed a personalized human-machine shared driving (HMSD) system that adapts to individual driving styles for safer obstacle avoidance. Trust varied, with cautious drivers showing the highest acceptance of this intelligent vehicle technology.

Keywords:
Driving styleGame theoryHuman–machine shared drivingIndividual differencesObstacle avoidancePersonalization

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Area of Science:

  • Intelligent Transportation Systems
  • Human-Machine Interaction
  • Automotive Engineering

Background:

  • Current human-machine shared driving (HMSD) systems lack personalization, potentially limiting their effectiveness and user acceptance.
  • Understanding individual driving styles is crucial for developing adaptive and intuitive driver assistance systems.

Purpose of the Study:

  • To develop and validate a personalized HMSD system that plans and tracks obstacle avoidance paths based on quantified driving styles.
  • To enhance driving safety, vehicle stability, and traffic efficiency through personalized driver assistance.

Main Methods:

  • Collected driver characteristic data using a driver-in-the-loop experimental bench.
  • Normalized and clustered data to quantify distinct driving styles.
  • Developed a personalized path planning algorithm and validated the system via experiments and surveys.

Main Results:

  • The personalized HMSD system effectively reduced driving load and improved driver-vehicle interaction, leading to high user satisfaction.
  • Significant individual differences in system trust were observed, with cautious drivers exhibiting higher trust than aggressive drivers.
  • The system demonstrated potential for improved driving safety, stability, and efficiency.

Conclusions:

  • Personalization of HMSD systems based on driving styles significantly enhances system acceptance and effectiveness.
  • Tailoring intelligent vehicle systems to individual user characteristics is key for future human-machine interaction development.
  • Future research should consider the nuanced trust dynamics between different driver types and intelligent systems.