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Deep Learning-Based Driver's Hands on/off Prediction System Using In-Vehicle Data.

Hyeongoo Pyeon¹, Hanwul Kim², Rak Chul Kim¹

¹Graduate School of Automotive Engineering, Kookmin University, Seoul 02707, Republic of Korea.

Sensors (Basel, Switzerland)

|February 11, 2023

Summary

This summary is machine-generated.

This study introduces a deep learning model for accurate driver hands on/off detection in autonomous vehicles. The system achieves 95.7% accuracy, improving safety and reliability in vehicle monitoring.

Keywords:

autonomous vehicle data collection system deep learning hands on/off state transition

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Area of Science:

Computer Science
Artificial Intelligence
Automotive Engineering

Background:

Driver monitoring is crucial for autonomous vehicle safety.
Existing hands on/off detection algorithms lack robustness and reliability.
Need for precise and dependable driver state assessment.

Purpose of the Study:

To propose a novel deep learning model for driver hands on/off detection using in-vehicle data.
To develop a reliable data collection system with auto-labeling.
To enhance the robustness and generalization ability of driver monitoring systems.

Main Methods:

Utilized a deep learning model trained on auto-labeled in-vehicle data.
Implemented a confidence logic to mitigate outlier influence.
Developed a new evaluation metric considering state transitions.
Conducted experiments with new drivers to assess generalization.

Main Results:

The proposed model achieved an average detection time of 0.37 seconds.
The system demonstrated a high accuracy of 95.7% in hands on/off detection.
Outperformed previous studies by addressing limitations in robustness and reliability.

Conclusions:

The developed deep learning system offers a more robust and reliable solution for driver hands on/off detection.
The model shows strong generalization capabilities, validated through experiments with new drivers.
This advancement contributes to enhanced safety in autonomous driving systems.