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A Study of Driver Cognitive Distraction Recognition in Turning and Straight Driving Scenarios
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Juan Zeng1, 2, 3, Bo Xu1, 2, 3, Hao Wang1, 2, 3, Hongchang Zhang1, 2, 3
Automobile Technology | 2025, (3) : 8 - 14
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Automobile Technology | 2025, (3): 8-14
• Special Topic on Multimodal Information Monitoring and Recognition Technologies for Human Factors in Intelligent Driving •
A Study of Driver Cognitive Distraction Recognition in Turning and Straight Driving Scenarios
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Juan Zeng1, 2, 3, Bo Xu1, 2, 3, Hao Wang1, 2, 3, Hongchang Zhang1, 2, 3
Affiliations
  • 1 Hubei Key Laboratory of Modern Automotive Components Technology, Wuhan University of Technology, Wuhan 430070
  • 2 Hubei Collaborative Innovation Center for Automotive Components Technology, Wuhan University of Technology, Wuhan 430070
  • 3 Hubei Engineering Research Center of New Energy and Intelligent Connected Vehicles, Wuhan University of Technology, Wuhan 430070
Published: 2025-03-24 doi: 10.19620/j.cnki.1000-3703.20230972
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In order to explore the underlying mechanisms of driver distraction in turning and straight driving scenarios, this study uses a driving simulator to create straight-driving and turning virtual scenarios. It also collects driving performance and eye-movement data of drivers in different driving states. The KNNImputer algorithm is employed to handle missing data during data collection. Then, a paired samples T test is used to analyze significant differences and extract significant difference feature indexes from sample data with a time window of 1 s length and 75% overlap. Based on these features, an XGBoost classifier is used to build cognitive distraction recognition models for different scenarios. The results show that compared with straight driving, drivers in turning scenarios have higher mental workload, indicated by lower pupil diameter change frequency, higher saccade speed and higher fixation duration percentage. The built cognitive distraction recognition model achieves an accuracy of 91.30% for straight-driving and 83.28% for turning scenarios. This suggests that cognitive distraction behavior in turning scenarios is more dangerous and harder to recognize.

Turning scenarios  /  Straight driving scenarios  /  Cognitive distraction  /  KNNImputer  /  XGBoost
Juan Zeng, Bo Xu, Hao Wang, Hongchang Zhang. A Study of Driver Cognitive Distraction Recognition in Turning and Straight Driving Scenarios[J]. Automobile Technology, 2025 , (3) : 8 -14 . DOI: 10.19620/j.cnki.1000-3703.20230972
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Year 2025 volume Issue 3
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doi: 10.19620/j.cnki.1000-3703.20230972
  • Online Date：2025-11-18
  • Published：2025-03-24
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  • Revised：2023-11-28
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Affiliations
    1 Hubei Key Laboratory of Modern Automotive Components Technology, Wuhan University of Technology, Wuhan 430070
    2 Hubei Collaborative Innovation Center for Automotive Components Technology, Wuhan University of Technology, Wuhan 430070
    3 Hubei Engineering Research Center of New Energy and Intelligent Connected Vehicles, Wuhan University of Technology, Wuhan 430070
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