  In a mixed traffic with connected automated vehicles (CAVs) and human-driven
vehicles (HDVs) coexisting, data-driven predictive control of CAVs promises
system-wide traffic performance improvements. Yet, most existing approaches
focus on a centralized setup, which is not computationally scalable 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 DeeP-LCC (Data-EnablEd Predictive Leading Cruise
Control) approach for CAVs to smooth mixed traffic flow. 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 burden with better safety performance,
while naturally preserving data privacy. Extensive traffic simulations validate
its wave-dampening ability, safety performance, and computational benefits.
