Applying Graph Neural Networks to Support Decision Making on Collective Intelligent Transportation Systems

Recent advancements in autonomous vehicles and vehicular ad-hoc networks (VANETs) have presented diverse solutions for vehicle safety and automation. The demand to establish a connection between the two worlds has increased significantly to augment road safety and provide benefits to end users. Intelligent Transportation Systems (ITSs) vehicle station leaves a signed trace of its geographic location that is rated as personal data. An attacker can misuse existing V2V communication to track a vehicle's CAM-trace and to avoid misusing CAMs to harm privacy, selectives communication approaches for CAMs should be chosen instead of continuous communication of current CAMs. In this article we propose a VANET topology learning methodology prioritizing anonymization that can use any existing Graph Learning framework. Further, this work enhances the quality of graph models applied to the context of VANETs and autonomous vehicles. With the real coordinates, establishment of a grid of cells and the generation and training of the graph, we can compare different frameworks generally used in these issues and one can chose the one that fits better to each scenario.