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The Identification of Non-Driving Activities with Associated Implication on the Take-Over Process.

Lichao Yang1, Mahdi Babayi Semiromi1, Yang Xing1

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Driver monitoring systems can identify non-driving activities (NDAs) using AI, revealing that these activities increase take-over time in automated driving. Haptic steering feedback can improve driver transition efficiency.

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Area of Science:

  • Human-Computer Interaction
  • Artificial Intelligence
  • Automotive Engineering

Background:

  • Driver engagement in non-driving activities (NDAs) is a critical factor influencing take-over performance in conditionally automated driving.
  • Understanding driver behavior is essential for designing intelligent human-machine interfaces that ensure safe and smooth control transitions.
  • NDAs can be categorized into active and passive modes based on driver-object interaction.

Purpose of the Study:

  • To develop and evaluate a 3D convolutional neural network (CNN) system for recognizing driver behaviors, including NDAs and driving activities.
  • To investigate the impact of different NDA types and interaction modes on driver situation awareness and take-over quality.
  • To explore the effectiveness of haptic feedback in assisting the driver take-over process.

Main Methods:

  • A 3D CNN model was trained using two video feeds analyzing head and hand movements.
  • The system classified six driver activities: four types of NDAs and two driving activities.
  • Driver performance metrics, including take-over time and lateral error, were analyzed under different NDA conditions.

Main Results:

  • The proposed recognition system achieved 85.87% accuracy in classifying six driver activities.
  • Engaging in NDAs, particularly active mode NDAs, significantly increased the time required for drivers to complete the control transition.
  • Active mode NDAs were found to be more mentally demanding, reducing driver sensitivity to driving situation changes.
  • Haptic feedback torque from the steering wheel was shown to reduce the transition time during take-over.

Conclusions:

  • AI-powered driver behavior recognition systems can effectively monitor non-driving activities in automated vehicles.
  • Non-driving activities negatively impact driver take-over performance, with active modes posing greater risks.
  • Haptic feedback presents a viable solution for enhancing driver transition efficiency and safety in automated driving systems.