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Road hypnosis, an unconscious driving state, can be effectively identified using a novel XGBoost-Hidden Markov model. This system enhances vehicle active safety by analyzing driver and vehicle data to detect impaired driving states.

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HMMXGBoostdriverroad hypnosisstate identificationvehicle

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Area of Science:

  • Road safety
  • Human-computer interaction
  • Artificial intelligence in automotive systems

Background:

  • Human factors are primary contributors to road traffic crashes.
  • Road hypnosis, an impaired driving state, significantly compromises driver perception and reaction times.
  • Active safety systems are crucial for mitigating human-caused traffic accidents.

Purpose of the Study:

  • To develop and validate a model for identifying road hypnosis.
  • To enhance the active safety of vehicles through real-time driving state monitoring.
  • To provide a technical framework for intelligent driving assistance systems.

Main Methods:

  • Collected driver data (eye movement, EEG) and vehicle data (speed, acceleration) during driving experiments.
  • Extracted dynamic features using power spectrum density analysis, sliding window, and point-by-point methods.
  • Developed an identification model integrating XGBoost and Hidden Markov algorithms with normalized feature vectors.

Main Results:

  • The proposed XGBoost-Hidden Markov model effectively identified the road hypnosis state.
  • Dynamic features related to road hypnosis were successfully extracted across non-fixed driving routes.
  • The study demonstrated the model's efficacy through visual analysis and evaluation.

Conclusions:

  • The developed model offers a significant advancement in identifying road hypnosis for improved vehicle active safety.
  • This research provides a new approach to studying road hypnosis and a reference for intelligent driving systems.
  • The findings contribute to enhancing the driver monitoring capabilities of intelligent vehicle cockpits.