Using Pi Zero SBCs as a Low-Cost Driver Monitoring Solution

Abstract

Driver monitoring systems have become a vital component of Advanced Driver Assistance Systems (ADAS) for vehicle safety. According to the U.S. National Highway Traffic Safety Administration, drowsy driving contributes to nearly 100,000 accidents annually. Early driver monitoring systems relied on vehicle sensor data, and current systems often use eye-tracking technology. Recently, there has been a rising interest in applying machine vision and deep learning to driver monitoring systems. Machine vision allows for advanced driver monitoring, such as detecting driver attention states, smartphone use, and seat belts, but machine vision systems typically require high processing power, which increases the device cost. This paper introduces a cost-effective driver monitoring system utilizing Pi Zero Single Board Computers (SBCs) like the Raspberry Pi Zero 2 W board, and Radxa Zero 3 W board, combined with a deep learning CNN, the system is capable of identifying various driver states, including safe driving, distraction, drowsiness, and smartphone use.