Cooperative Exchange-Based Platooning Using Predicted Fuel-Optimal Operation of Heavy-Duty Vehicles

Christian Earnhardt, Ben Groelke, John Borek, Evan Pelletier, Sean Brennan, Chris Vermillion

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


Several driving situations exist where fuel-optimal driving in terms of aggregate performance can only be achieved when one or more vehicles incurs a sacrifice in its own fuel consumption. For these situations, an economic incentive is needed to entice that vehicle to participate in aggregate fuel-optimal driving. Focusing on platooning amongst automated heavy-duty vehicles and using real trucking routes, we examine the precise extent to which the benefits of platooning can be expanded through the incorporation of exchange-based incentives. We focus on two mechanisms for incentivized platooning: (i) incentivized 'catch-up' along a prescribed highway route and (ii) incentivized re-routing to allow for platooning. For the incentivized 'catch-up' mechanism, platoon capable vehicles begin at staggered positions, using a novel platoon catch-up algorithm capable of determining the fuel-optimal platoon engagement position and fuel-optimal velocity trajectories. Additionally, the incentivized re-routing mechanism determines the optimal route for a network of platoon-capable vehicles, allowing for a vehicle to reroute its trajectory to engage within the platoon. Because such scenarios will be shown to frequently lead to aggregate benefit, while actually hurting the fuel economy of one or more participants, we propose three methods for explicitly computing the monetary value of the exchange. Assuming a known trajectory and traffic pattern, the first uses the Shapley value to determine the exchange value. The second method adjusts the Shapley value, accounting for uncertainty associated with traffic modeling. The final method assumes a competitive market, requiring each individual operator to implement a bid.

Original languageEnglish (US)
Pages (from-to)17312-17324
Number of pages13
JournalIEEE Transactions on Intelligent Transportation Systems
Issue number10
StatePublished - Oct 1 2022

All Science Journal Classification (ASJC) codes

  • Automotive Engineering
  • Mechanical Engineering
  • Computer Science Applications


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