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Cooperative control of Connected and Autonomous Vehicles (CAVs) promisesgreat benefits for mixed traffic. Most existing research focuses on model-basedcontrol strategies, assuming that car-following dynamics of human-drivenvehicles are explicitly known. In this paper, instead of relying on aparametric car-following model, we introduce a data-driven predictive controlstrategy to achieve safe and optimal control for CAVs in mixed traffic. Wefirst present a linearized dynamical model for mixed traffic systems, andinvestigate its controllability and observability. Based on thesecontrol-theoretic properties, we then propose a novel DeeP-LCC (Data-EnablEdPredictive Leading Cruise Control) strategy for CAVs based on measurabledriving data to smooth mixed traffic. Our method is implemented in a recedinghorizon manner, in which input/output constraints are incorporated to achievecollision-free guarantees. Nonlinear traffic simulations reveal its saving ofup to 24.96% fuel consumption during a braking scenario of Extra-Urban DrivingCycle while ensuring safety.

Data-Driven Predictive Control for Connected and Autonomous Vehicles in Mixed Traffic