An effective IoT interface considering an eye-tracking method for autonomous vehicle

Abstract

The number of Internet-connected devices is steadily increasing, exceeding 25 billion globally and projected to surpass 50 billion about 6.5 times the world population. At the core of IoT is data collection via sensors, forming big data for AI-driven analysis, optimization, and visualization. A convenient control environment is essential for devices like autonomous vehicles, requiring new interfaces such as touchless eye movement. This research proposes a real-time eye-tracking method using a single web camera, easily installed in cars and integrated with IoT. The system detects gaze by recognizing iris shape, enabling software-only tracking without extra hardware. Experiments show a mean absolute error (MAE) of 3.49°, ensuring accuracy even with head movement. Unlike existing infrared (IR) LED or head-mounted methods, this approach offers a cost-effective, real-time solution. Using lightweight image processing instead of deep learning, the system achieves real-time tracking with low latency, making it ideal for low-power IoT and autonomous vehicles. It is expected to become a next-generation input interface for these applications.