Deep Neural Network Heuristic Hierarchization for Cooperative Intelligent Transportation Fleet Management
Qiao Ke, Jakub Siłka, Michał Wieczorek, Zongwen Bai, Marcin Woźniak
Abstract
In this article, we propose malfunction classifications for trucks, a novel idea for smart fleet management systems. In the proposed cooperative cooperative intelligent transportation (C-ITS), the developed neural network work with information from truck fleets to select the trucks that need a service. From the results returned from the deep neural network classifier, the applied heuristic algorithm uses the classification outputs to select the most important results. The proposed process is multithreaded; thus, the composed system gains additional efficiency. The implemented deep learning model achieved an accuracy above 98%, and an above 95% recall. The developed solution was tested on the Scania Truck data collection. The research results show the importance of the advances and validate our concept for potential further development.