Privacy-Preserved Data-Enabled Predictive Control for Connected and Automated Vehicles in Mixed Traffic

Data-driven predictive control has emerged as a promising model-free paradigm when an explicit system model is difficult or expensive to obtain. However, this framework may be vulnerable to privacy leakage since data sharing is needed. In this paper, we empower it with privacy protection and develop a privacy-preserved data-enabled predictive control scheme for cooperative cruise control of connected and automated vehicles (CAVs) in a mixed traffic environment. We tackle external eavesdroppers who wiretap the communication channel of the mixed traffic system and intend to infer the CAVs' state and input information. An affine masking-based privacy protection method is developed to conceal the true state and input signals, and an extended form of the data-enabled predictive control is derived to achieve privacy-preserved optimal control for CAVs. Our proposed scheme can protect the privacy of CAVs against external eavesdroppers without affecting the control performance or incurring heavy computations. Numerical simulations confirm the efficacy of the proposed approach.