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An Identification Method for Road Hypnosis Based on the Fusion of Human Life Parameters
Bin Wang1, Jingheng Wang2, Xiaoyuan Wang1
1College of Electromechanical Engineering, Qingdao University of Science and Technology, Qingdao 266000, China.
Sensors (Basel, Switzerland)
|December 17, 2024
Summary
This study introduces a new method for identifying road hypnosis by fusing eye movement and electroencephalogram (EEG) data. The developed model effectively detects road hypnosis, enhancing driver safety.
Area of Science:
- Neuroscience
- Human-Computer Interaction
- Transportation Safety
Background:
- Road hypnosis is a dangerous state of reduced awareness during driving.
- Identifying road hypnosis relies on external (e.g., eye movement) and internal (e.g., electroencephalogram - EEG) characteristics.
- Existing methods may not fully capture the complex nature of road hypnosis.
Purpose of the Study:
- To propose a novel method for road hypnosis identification by fusing human life parameters.
- To develop a robust model integrating eye movement and EEG data for improved detection accuracy.
- To enhance understanding of the mechanisms underlying road hypnosis.
Main Methods:
- Collected eye movement and EEG data from driving experiments.
- Preprocessed data using Principal Component Analysis (PCA) and Independent Component Analysis (ICA).
- Trained eye movement data with a Self-Attention Model (SAM) and EEG data with a Deep Belief Network (DBN), then fused using stacking.
Main Results:
- The fused model effectively recognized road hypnosis.
- The proposed method demonstrated high accuracy in identifying this driving state.
- Repeated Random Subsampling Cross-Validation (RRSCV) confirmed model reliability.
Conclusions:
- Fusion of eye movement and EEG data provides an effective approach for road hypnosis identification.
- The developed SAM-DBN stacking model significantly improves detection accuracy.
- This research contributes to understanding road hypnosis and enhancing driver safety systems.
