TinyML for Safe Driving: The Use of Embedded Machine Learning for Detecting Driver Distraction

Among the main causes of road accidents, one of the most significant is related to driver distraction while driving, which is responsible for 18% of car accidents worldwide. This situation has demanded the development of mechanisms to automatically detect this dangerous behavior while driving. One of the computational solutions that has been considered viable to detect situations like this is the use of Convolutional Neural Networks (CNN), but some complex issues arise when deploying CNN models in microcontroller-based embedded devices with constrained processing and memory capabilities. In this context, this paper proposes a driver distraction detection system that achieves high accuracy (99.3%) and low latency (72ms) while requiring minimal computational resources (Peak- RAM of 164 KB and Flash of 52.7 KB). This solution exploiting Tiny Machine Learning (TinyML) algorithms was developed with the support of the Edge Impulse platform, used to perform the entire ML pipeline, from data pre-processing and ML model creation to deployment into an Arduino Portenta H7 board. By designing a driver assistance system that can be integrated into vehicles, it is expected that an affordable solution based on embedded machine learning is provided, tackling a real-world problem by potentially reducing accidents caused by driver distractions.

Automated summary

This paper proposes a developed driver distraction detection system that achieves high accuracy and low latency, while requiring minimal computational resources. The system utilizes TinyML algorithms and is developed with the support of the Edge Impulse platform, deployed on an Arduino Portenta H7 board.