CNN-Based Real-Time Driver Behaviour Monitoring for Petroleum Product Transportation: Trends, Challenges, and Prospects

ADENEKAN Wasiu Ademola, AJAYI Binyamin Adeniyi, CHOJI Davou Nyap

Abstract: The national logistics systems are very critical and high-risk, specifically in the transportation of petroleum products. This is to a larger extent in developing economies such as Nigeria, where road tankers dominate the distribution of fuels. Fatigue, distraction, and intoxication are the primary causes of most tanker accidents, which affects human safety, infrastructure, and the environment. The recent innovations in the CNN-based systems of real-time monitoring of driver behavior are critically discussed in this paper with a specific emphasis on the potential use of the given innovations in the context of petroleum logistics challenges and mitigation of human-associated risks. The review identifies the key tendencies in the deep learning field in using hybrid CNN-LSTM and CNN-Transformer networks capable of capturing spatial and temporal dynamics of behavior and multimodal networks capable of fusing visual, telemetric, and physiological information to enhance robustness. The use of edge computing and federated learning was also explored so that it may be applied in the context of real-time monitoring to minimize latency and maintain privacy. Despite enormous gains in the study, there are still challenges like the shortage of specialized petroleum information, computing capability restrictions in embedded systems, changes in the environment, and a lack of impartiality in the regulations. To offer a solution to the existing problems, the paper suggests a network of CNN, YOLOv8, and a Finite State machine that integrates both proactive safety management, which includes advanced deep learning and temporal reasoning, with practical testing, Explainable AI (XAI) integration, and regulatory systems together with the Federal Road Safety Corps (FRSC). Overall, the review demonstrates that a CNN-based driver-behavior monitoring system, when it is ethically designed and institutionally established, creates a pathway that can provide a potential solution to intelligent, data-driven, and sustainable transport safety of petroleum.

Keywords: Convolutional neural network, CNN, Driving behavior monitoring, petroleum transportation, Realtime Systems, ITS, Intelligent Transport Systems, Deep learning, XAI, Explainable AI, FRSC