Decentralized Robust Data-Driven Predictive Control for Smoothing Mixed Traffic Flow

In a mixed traffic with connected automated vehicles (CAVs) and human-driven vehicles (HDVs), data-driven predictive control of CAVs promises system-wide traffic performance improvements. Yet, most existing approaches focus on a centralized setup, which is computationally unscalable while failing to protect data privacy. The robustness against unknown disturbances has not been well addressed either, causing safety concerns. In this paper, we propose a decentralized robust <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DeeP-LCC</monospace> (Data-EnablEd Predictive Leading Cruise Control) approach for CAVs to smooth mixed traffic. In particular, each CAV computes its control input based on locally available data from its involved subsystem. Meanwhile, the interaction between neighboring subsystems is modeled as a bounded disturbance, for which appropriate estimation methods are proposed. Then, we formulate a robust optimization problem and present its tractable computational solutions. Compared with the centralized formulation, our method greatly reduces computation complexity with better safety performance, while naturally preserving data privacy. Extensive traffic simulations validate its wave-dampening ability, safety performance, and computational benefits.