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Driver Distraction Recognition Using Wearable IMU Sensor Data

Author

Listed:
  • Wencai Sun

    (School of Transportation, Jilin University, 5988 Renmin Street, Changchun 130022, China)

  • Yihao Si

    (School of Transportation, Jilin University, 5988 Renmin Street, Changchun 130022, China)

  • Mengzhu Guo

    (School of Transportation, Jilin University, 5988 Renmin Street, Changchun 130022, China)

  • Shiwu Li

    (School of Transportation, Jilin University, 5988 Renmin Street, Changchun 130022, China)

Abstract

Distracted driving has become a major cause of road traffic accidents. There are generally four different types of distractions: manual, visual, auditory, and cognitive. Manual distractions are the most common. Previous studies have used physiological indicators, vehicle behavior parameters, or machine-visual features to support research. However, these technologies are not suitable for an in-vehicle environment. To address this need, this study examined a non-intrusive method for detecting in-transit manual distractions. Wrist kinematics data from 20 drivers were collected using wearable inertial measurement units (IMU) to detect four common gestures made while driving: dialing a hand-held cellular phone, adjusting the audio or climate controls, reaching for an object in the back seat, and maneuvering the steering wheel to stay in the lane. The study proposed a progressive classification model for gesture recognition, including two major time-based sequencing components and a Hidden Markov Model (HMM). Results show that the accuracy for detecting disturbances was 95.52%. The accuracy associated with recognizing manual distractions reached 96.63%, using the proposed model. The overall model has the advantages of being sensitive to perceptions of motion, effectively solving the problem of a fall-off in recognition performance due to excessive disturbances in motion samples.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:3:p:1342-:d:488380
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    References listed on IDEAS

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    1. 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.
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    Cited by:

    1. Petar Čolić & Marijan Jakovljević & Krešimir Vidović & Marko Šoštarić, 2022. "Development of Methodology for Defining a Pattern of Drivers Mobile Phone Usage While Driving," Sustainability, MDPI, vol. 14(3), pages 1-28, February.

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