Computing Feasible Vehicle Platooning Opportunities for Transport Assignments

TL;DR

This paper presents a scalable centralized method to identify feasible vehicle platooning opportunities in large transport fleets by using feature-based filtering to efficiently narrow down candidate pairs for detailed analysis.

Contribution

It introduces a feature extraction approach that efficiently filters vehicle pairs, enabling scalable identification of platooning opportunities in large fleets.

Findings

The approach scales well to large vehicle fleets and realistic road networks.

Feature-based filtering reduces the number of pairs requiring detailed analysis.

Simulation demonstrates the effectiveness of the method.

Abstract

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.