Distracted driving is a critical safety issue that leads to numerous fatalities and injuries worldwide. This study addresses the urgent need for efficient and real-time machine learning models to detect distracted driving behaviors. Leveraging the Pretrained YOLOv8 (P-YOLOv8) model, a real-time object detection system is introduced, optimized for both speed and accuracy. This approach addresses the computational constraints and latency limitations commonly associated with conventional detection models. The study demonstrates P-YOLOv8 versatility in both object detection and image classification tasks using the Distracted Driver Detection dataset from State Farm, which includes 22,424 images across ten behavior categories. Our research explores the application of P-YOLOv8 for image classification, evaluating its performance compared to deep learning models such as VGG16, VGG19, and ResNet. Some traditional models often struggle with low accuracy, while others achieve high accuracy but come with high computational costs and slow detection speeds, making them unsuitable for real-time applications. P-YOLOv8 addresses these issues by achieving competitive accuracy with significant computational cost and efficiency advantages. In particular, P-YOLOv8 generates a lightweight model with a size of only 2.84 MB and a lower number of parameters, totaling 1,451,098, due to its innovative architecture. It achieves a high accuracy of 99.46 percent with this small model size, opening new directions for deployment on inexpensive and small embedded devices using Tiny Machine Learning (TinyML). The experimental results show robust performance, making P-YOLOv8 a cost-effective solution for real-time deployment. This study provides a detailed analysis of P-YOLOv8's architecture, training, and performance benchmarks, highlighting its potential for real-time use in detecting distracted driving.

The National Safety Council says at least nine people in the U.S. die and another 100 are injured every day in crashes caused by distracted driving. In-vehicle technologies such as dashboard touchscreens have contributed to this enormous safety threat. But consumers are fond of these technologies and they aren't going away. However, there is a pantheon of other distractions that occur behind the wheel that vary greatly in form and severity. The mostly illegal use of cell phones or texting while driving tops the list.

Everyone knows that distracted driving is a problem, but it tends to fall in the "other people/not me" category of personal risk assessment among drivers. But when you consider that a staggering 80 percent of traffic accidents--and 17 percent of fatalities--are caused by distracted driving, according to the National Highway Traffic Safety Administration, that's clearly flawed logic, by any measure. But while we're confident that self-driving cars are on their way to save us from ourselves--however slowly--until they do arrive we have to deal with the fact that people are texting, tweeting, and just generally smartphoning at the wheel. But a group of Canadian researchers think they can outwit those overconfident oversharers with the help of artificial intelligence. A team at the University of Waterloo's Centre for Pattern Analysis and Machine Intelligence has developed software that can determine when drivers are texting or otherwise distracted--a potentially crucial step toward halting the habit. "Driver distraction is a growing problem," says program director Fakhri Karray, who studies electrical and computer engineering.

Self-driving cars continued to make headlines recently. Google (Waymo) opened its early rider program in Phoenix, Arizona, to "hundreds" of residents "with diverse backgrounds and transportation needs." Baidu open sourced its self-driving technology and said it's on track to deliver self-driving cars by the end of 2020. And documents obtained by Business Insider and the Wall Street Journal revealed Apple has hired former NASA employees, robotics experts and ex-Tesla staffers to form part of its driverless car teams. The news came during the National Safety Council's Distracted Driving Awareness month.