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Driver Cognitive Distraction Detection Using Driving Performance Measures

Author

Listed:
  • Lisheng Jin
  • Qingning Niu
  • Haijing Hou
  • Huacai Xian
  • Yali Wang
  • Dongdong Shi

Abstract

Driver cognitive distraction is a hazard state, which can easily lead to traffic accidents. This study focuses on detecting the driver cognitive distraction state based on driving performance measures. Characteristic parameters could be directly extracted from Controller Area Network-(CAN-)Bus data, without depending on other sensors, which improves real-time and robustness performance. Three cognitive distraction states (no cognitive distraction, low cognitive distraction, and high cognitive distraction) were defined using different secondary tasks. NLModel, NHModel, LHModel, and NLHModel were developed using SVMs according to different states. The developed system shows promising results, which can correctly classify the driver’s states in approximately 74%. Although the sensitivity for these models is low, it is acceptable because in this situation the driver could control the car sufficiently. Thus, driving performance measures could be used alone to detect driver cognitive state.

Suggested Citation

  • Lisheng Jin & Qingning Niu & Haijing Hou & Huacai Xian & Yali Wang & Dongdong Shi, 2012. "Driver Cognitive Distraction Detection Using Driving Performance Measures," Discrete Dynamics in Nature and Society, Hindawi, vol. 2012, pages 1-12, November.
    • Handle: RePEc:hin:jnddns:432634
    DOI: 10.1155/2012/432634
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    Cited by:

    1. Amin Azimian & Carlos Alberto Catalina Ortega & Juan Maria Espinosa & Miguel Ángel Mariscal & Susana García-Herrero, 2021. "Analysis of Drivers’ Eye Movements on Roundabouts: A Driving Simulator Study," Sustainability, MDPI, vol. 13(13), pages 1-10, July.
    2. Wencai Sun & Yihao Si & Mengzhu Guo & Shiwu Li, 2021. "Driver Distraction Recognition Using Wearable IMU Sensor Data," Sustainability, MDPI, vol. 13(3), pages 1-17, January.

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