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

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