A Novel Deep Learning-Assisted Truck Driver Drowsiness Record using Blockchain Technology

This study introduces an innovative approach involving Deep Learning-Assisted Truck Driver Drowsiness Record using Blockchain Technology. It surpasses previous research by not only classifying driver drowsiness through deep learning techniques but also implementing blockchain technology for secure data management. Focusing on trucks in logistics, the research presents a hybrid system that combines deep learning and blockchain to classify and securely record instances of driver drowsiness, thereby providing timely warnings to prevent accidents. The blockchain’s immutable and tamper-resistant nature ensures the credibility of recorded data. This system enables truck drivers to access their drowsiness records via mobile devices, allows logistic companies to evaluate driver performance, and permits insurance companies to adjust premiums based on drowsiness occurrences. The research evaluates three deep learning algorithms (VGG-16, MobileNet, CNN) achieving 50% accuracy each on a grayscale image driver drowsiness dataset. Additionally, smart contracts are employed for the blockchain storage of drowsiness records.