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A game-theoretic driver steering model with individual risk perception field generation.

Wenfeng Guo1, Jun Li1, Xiaolin Song2

  • 1School of Vehicle and Mobility, Tsinghua University, Beijing 10084, China.

Accident; Analysis and Prevention
|December 4, 2024
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Summary

This study introduces a game-theoretic model for driver-automation shared steering control (SSC), incorporating individual risk perception. The model accurately predicts driver steering behavior and individual differences in risk perception.

Keywords:
Driver steering behaviorDriving risk perceptionExperimental evaluationGame theoryShared steering control

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Area of Science:

  • Automotive Engineering
  • Human-Computer Interaction
  • Control Theory

Background:

  • Driver-automation shared steering control (SSC) is crucial for vehicle safety.
  • Understanding driver steering behavior during human-automation interaction is essential for seamless collaboration.

Purpose of the Study:

  • To develop a game-theoretic driver steering model that incorporates individual risk perception.
  • To quantitatively estimate driver's perceived driving risk using a novel potential injury risk (PIR) concept.
  • To model the steering interaction between driver and automation using a non-cooperative Nash game framework.

Main Methods:

  • Development of a driver risk perception field based on potential injury risk (PIR).
  • Integration of the risk perception field into a non-cooperative Nash game to model steering interaction.
  • Derivation of the analytical expression for Nash equilibrium.
  • Parameter identification using driver steering behavior data from 30 subjects in simulator experiments.

Main Results:

  • The combined driver model effectively captures driver adaptation at control and planning levels.
  • The model demonstrates the lowest prediction errors compared to existing models.
  • The model successfully captures individual differences in risk perception and steering behavior.

Conclusions:

  • The proposed game-theoretic driver steering model with individual risk perception field generation is effective and superior.
  • This model enhances the understanding of driver steering behavior in shared steering control scenarios.
  • The findings contribute to the development of safer and more collaborative autonomous driving systems.