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Sleepy driving still plays a significant role in the number of accidents and fatalities that occur each year, making road safety still a major global concern. A low-cost embedded platform for a vision-based, real-time drowsiness detection system will be implemented using a Raspberry Pi 4B and Pi Camera module. Continuously monitoring the driver’s facial features by measuring metrics such as yawning and extended eye closure can enable the system to intervene promptly and avert accidents. This solution brings state-of-the-art safety features for cars regardless of class and is unique due to its affordability, portability, and scalability.

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JEEVA RAKSHAKA 1.0-AI-Driven Vision-Based Drowsiness Detection System for Driver Safety and Accident Prevention

  • 会议论文

  • 发表日期：

    2026年

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  • B. Perumal,
  • Saikumar Rotikadi,
  • Hareeswar Sarma Mv,
  • T. Gangadhar Reddy,
  • M. Pallikonda Rajasekaran,
  • Saravanan Velusamy

摘要

Sleepy driving still plays a significant role in the number of accidents and fatalities that occur each year, making road safety still a major global concern. A low-cost embedded platform for a vision-based, real-time drowsiness detection system will be implemented using a Raspberry Pi 4B and Pi Camera module. Continuously monitoring the driver’s facial features by measuring metrics such as yawning and extended eye closure can enable the system to intervene promptly and avert accidents. This solution brings state-of-the-art safety features for cars regardless of class and is unique due to its affordability, portability, and scalability.