Privacy Preserving and Federated Learning-based Drowsy Driving Detection for ITS

The issue of drowsy driving is highly dangerous for road safety and lead to car accidents, injuries as well as deaths. This study examines the several technology suggestions made to address the serious issue of drowsy driving, which compromises road safety worldwide. The study looks at different sleep-related accident prevention tactics and assesses how well they work with intelligent transportation systems (ITS). Federated Learning (FL) is an intriguing method that ensures privacy while improving accuracy for identifying weariness. Furthermore, the paper offers a novel method of transparently responsible traffic control by fusing blockchain technology with convolutional neural networks (CNNs). This integrated system authenticates smart contracts, maintains stringent records, and quickly detects drowsiness to protect system integrity. Essentially, these results highlight effective learning dynamics in FL, like strong aggregation techniques. Furthermore, this fully integrated architecture is not only exceptionally good at detecting sleep, but it also boasts a safe and dependable environment that is applicable to a variety of sectors in terms of international settings and the dependability of academic discourse.