Development of a Real-Time Driver Fatigue Warning System Using Edge Computing

Abstract

Driver fatigue is a significant factor contributing to traffic accidents, necessitating the development of efficient detection systems. This study proposes a vehicle fatigue driving warning system leveraging edge computing to address latency and security challenges associated with traditional cloud-based approaches. The system utilizes the Ruixin RV1126 chip as its data processing core, enabling millisecond-level detection of fatigue through real-time processing at the edge. By analyzing facial features such as eye closure, mouth opening, and head sway captured via onboard cameras, the system identifies fatigue states and issues immediate warnings to drivers. Key advantages of this approach include low cost, high recognition speed, and robust accuracy, demonstrating substantial practical value in enhancing driving safety.