Applying Real World Traffic Data on Machine Learning Methods for Graph Based Cooperation Strategies of Automated Vehicles

With the increasing distribution of smart mobility systems, automated & connected vehicles are more and more interacting with each other and with smart infrastructure using V2X-communication. Hereby, the vehicles' position, driving dynamics data, or driving intention are exchanged. Previous research has explored graph-based cooperation strategies for automated vehicles in mixed traffic environments based on current V2X-communication standards. Thereby, the focus is set on cooperation optimization and maneuver negotiation. These strategies can be implemented through both centralized and decentralized computational approaches and are conflict-free by design. To enhance these previously established cooperation models, real-world traffic data is used to derive vehicle trajectories, providing a more accurate representation of actual traffic scenarios in order to enhance the practical application of the described methodology. Additionally, machine learning algorithms are employed to train the cooperation algorithms with the derived trajectories, enabling further optimization of traffic flow in direct vehicle to vehicle interaction. The application of different machine learning techniques allows for a comparison of their performance in optimizing traffic scenarios with regard to each other and conventional optimization approaches. This study builds on prior work, integrating real-world data and advanced algorithms to refine cooperative strategies for automated vehicles, ultimately improving the efficiency of smart mobility systems as shown by a multi-vehicle simulation. This approach shows the potential for enhanced cooperative distributed vehicle control and thereby increases safety and efficiency in such transportation networks significantly. Hereby, the overall cooperation scenario is optimized with regard to the cumulated maneuver execution time and energy consumption of the involved automated and connected vehicles.