Detection of Distracted Driver Using Deep Learning Algorithm

Driving a car is a challenging task that requires your full attention. Any action that causes the motorist to lose focus on the road constitutes distracted driving.1.35 million people perish in automobile accidents occur annually. Approximately one out of every six car accidents are caused by distracted driving, according to the National Highway Traffic Safety Administration. We want to build a system that can identify distracted drivers and alert them. To accomplish these goals, we gathered a wide range of data that included instances of distracted driving activities, such as texting, making phone calls, eating, and adjusting the radio. To guarantee consistency and suitability for model training, the dataset underwent preparation and was assigned tags with matching class labels. After that, we created a convolutional neural network (CNN) architecture that was enhanced for image classification tasks by using transfer learning strategies. The outcomes of the study show how well the suggested method works in real time for recognizing distracted driving behaviors. Over 90% classification accuracy was achieved by the trained model on a held-out test dataset, exceeding baseline techniques and proving its usefulness in real-world situations. Additionally, we put in place an intervention mechanism that will promptly notify drivers when distracted driving behaviors are identified, raising awareness and supporting safer driving practices. All things considered, this research advances the development of devices meant to lower the prevalence of distracted driving and improve traffic safety. We offer a viable framework for tackling this important problem and possibly saving lives along the road by utilizing deep learning methods for real-time detection and intervention.