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A Proactive Recognition System for Detecting Commercial Vehicle Driver's Distracted Behavior.

Xintong Yan1, Jie He1, Guanhe Wu1

  • 1School of Transportation, Southeast University, Nanjing 210018, China.

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|March 26, 2022
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This study enhances driver distraction detection systems by analyzing posture characteristics. Cascaded Convolutional Neural Network (CNN) models improve recognition accuracy for commercial vehicle drivers, especially at night.

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commercial vehicle surveillance systemdeep learning approachesdriver’s distracted behaviorproactive recognition system

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Transportation Safety

Background:

  • Road traffic accidents involving commercial vehicles significantly impact economic development, often linked to driver distraction.
  • Current driver distraction surveillance systems have limitations in recognizing diverse objects, scenarios, and behaviors.
  • Driver posture is a key indicator of distracted behavior, necessitating advanced monitoring techniques.

Purpose of the Study:

  • To develop a comprehensive framework for recognizing driver's distracted behavior by expanding recognition objects, scenarios, and types.
  • To analyze driver posture characteristics for improved modeling of distracted behavior.
  • To evaluate the performance of Convolutional Neural Network (CNN) models in identifying distracted driving postures.

Main Methods:

  • Driver posture characteristics were analyzed to form the basis for modeling.
  • Five CNN sub-models were developed for different posture categories.
  • A holistic multi-cascaded CNN framework was implemented and tested on extensive daytime and nighttime image datasets.

Main Results:

  • Cascaded CNN models (both daytime and nighttime) outperformed non-cascaded models in performance.
  • Nighttime models demonstrated faster processing speeds but lower accuracy compared to daytime models.
  • The proposed framework significantly enhances the recognition capabilities for driver's distracted behavior.

Conclusions:

  • The developed cascaded CNN framework offers a more effective approach to monitoring driver distraction.
  • Findings provide valuable insights for designing real-time driver monitoring and warning systems.
  • Future work should integrate vehicle state parameters with driver behavior for proactive surveillance systems.