  Cooperative control of connected and automated vehicles (CAVs) promises
smoother traffic flow. In mixed traffic, where human-driven vehicles with
unknown dynamics coexist, data-driven predictive control techniques allow for
CAV safe and optimal control with measurable traffic data. However, the
centralized control setting in most existing strategies limits their
scalability for large-scale mixed traffic flow. To address this problem, this
paper proposes a cooperative DeeP-LCC (Data-EnablEd Predictive Leading Cruise
Control) formulation and its distributed implementation algorithm. In
cooperative DeeP-LCC, the traffic system is naturally partitioned into multiple
subsystems with one single CAV, which collects local trajectory data for
subsystem behavior predictions based on the Willems' fundamental lemma.
Meanwhile, the cross-subsystem interaction is formulated as a coupling
constraint. Then, we employ the Alternating Direction Method of Multipliers
(ADMM) to design the distributed DeeP-LCC algorithm. This algorithm achieves
both computation and communication efficiency, as well as trajectory data
privacy, through parallel calculation. Our simulations on different traffic
scales verify the real-time wave-dampening potential of distributed DeeP-LCC,
which can reduce fuel consumption by over 31.84% in a large-scale traffic
system of 100 vehicles with only 5%-20% CAVs.
