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Adaptive driver following model that integrates perception process and driving behavior.

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This study introduces an adaptive model for intelligent vehicles to simulate driver behavior, enhancing human-computer interaction. The model accurately captures driver characteristics, improving following performance by 80%.

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Area of Science:

  • Intelligent Transportation Systems
  • Human-Computer Interaction
  • Control Systems Engineering

Background:

  • Intelligent vehicles require personalized human-computer interaction to meet user needs in China.
  • Simulating driver following behavior is crucial for improving vehicle safety and user satisfaction.

Purpose of the Study:

  • To design an adaptive longitudinal following model for intelligent vehicles.
  • To integrate perceptual processes and driver characteristics for realistic behavior simulation.
  • To enhance human-computer interaction and collaboration in autonomous driving.

Main Methods:

  • Utilized Wiener process to model the randomness of driver perception for speed and distance.
  • Developed a driver characteristic parameter identification algorithm for personalized following.
  • Implemented a fuzzy-tuned sliding mode control system for driver simulation.

Main Results:

  • The model effectively recognizes individual driver characteristics.
  • Driver following behavior is simulated with high fidelity.
  • The adaptive following model improved the following index by 80%.

Conclusions:

  • The proposed model enhances the simulation of driver following behavior in intelligent vehicles.
  • Personalized driver characteristics integration leads to improved interaction and acceptance.
  • This research contributes to more intuitive and safer human-machine collaboration in autonomous driving.