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Cooperative control of connected and automated vehicles (CAVs) promisessmoother traffic flow. In mixed traffic, where human-driven vehicles withunknown dynamics coexist, data-driven predictive control techniques allow forCAV safe and optimal control with measurable traffic data. However, thecentralized control setting in most existing strategies limits theirscalability for large-scale mixed traffic flow. To address this problem, thispaper proposes a cooperative DeeP-LCC (Data-EnablEd Predictive Leading CruiseControl) formulation and its distributed implementation algorithm. Incooperative DeeP-LCC, the traffic system is naturally partitioned into multiplesubsystems with one single CAV, which collects local trajectory data forsubsystem behavior predictions based on the Willems' fundamental lemma.Meanwhile, the cross-subsystem interaction is formulated as a couplingconstraint. Then, we employ the Alternating Direction Method of Multipliers(ADMM) to design the distributed DeeP-LCC algorithm. This algorithm achievesboth computation and communication efficiency, as well as trajectory dataprivacy, through parallel calculation. Our simulations on different trafficscales verify the real-time wave-dampening potential of distributed DeeP-LCC,which can reduce fuel consumption by over 31.84% in a large-scale trafficsystem of 100 vehicles with only 5%-20% CAVs.

Distributed data-driven predictive control for cooperatively smoothing mixed traffic flow