Detection of distracted driving via edge artificial intelligence

Real-time detection and alert systems for distracted driving are pivotal areas of research. With Edge AI, it is feasible to process data in real-time without relying on Internet connectivity, thereby safeguarding user privacy. However, the computational and storage constraints of edge devices can hamper the deployment of deep learning models, such as VGG-16. In response, we introduce a framework tailored for distracted driving detection using an ensemble model. This model taps into the potential of lightweight pre-trained networks like Inception, Xception, DenseNet, and MobileNet. Our objective is to amalgamate these models to achieve high accuracy, low latency, and fewer parameter set. Experiments on the State Farm dataset reveal a remarkable 99% accuracy rate, underlining its viability for devices with limited resources, such as the Nvidia Jetson Nano. These results underscore the efficacy and feasibility of our framework in the realm of edge computing.