Computing Feasible Vehicle Platooning Opportunities for Transport Assignments
For centralized platooning coordination systems, this method enables efficient identification of platooning opportunities in large-scale transport assignments.
The paper presents a scalable method to identify feasible vehicle platooning opportunities by extracting features from transport assignments, ruling out most vehicle pairs, and using a complete algorithm for remaining pairs. Simulation shows the approach effectively handles large fleets and realistic road networks.
Vehicle platooning facilitates the partial automation of vehicles and can significantly reduce fuel consumption. Mobile communication infrastructure makes it possible to dynamically coordinate the formation of platoons en route. We consider a centralized system that provides trucks with routes and speed profiles allowing them to dynamically form platoons during their journeys. For this to work, all possible pairs of vehicles that can platoon based on their location, destination, and other constraints have to be identified. The presented approach scales well to large vehicle fleets and realistic road networks by extracting features from the transport assignments of the vehicles and rules out a majority of possible pairs based on these features only. Merely a small number of remaining pairs are considered in depth by a complete and computationally expensive algorithm. This algorithm conclusively decides if platooning is possible for a pair based on the complete data associated with the two vehicles. We derive appropriate features for the problem and demonstrate the effectiveness of the approach in a simulation example.