DeeP-LCC: Data-EnablEd Predictive Leading Cruise Control in Mixed Traffic Flow



In this article, instead of relying on a parametric car-following model, we introduce a data-driven nonparametric strategy, called Data-EnablEd Predictive Leading Cruise Control (DeeP-LCC), to achieve safe and optimal control of CAVs in mixed traffic. We first utilize Willems’ fundamental lemma to obtain a data-centric representation of mixed traffic behavior. This is justified by rigorous analysis on controllability and observability properties of mixed traffic. We then employ a receding horizon strategy to solve a finite-horizon optimal control problem at each time step, in which input–output constraints are incorporated for collision-free guarantees. Numerical experiments validate the performance of DeeP-LCC compared to a standard predictive controller that requires an accurate model. Multiple nonlinear traffic simulations further confirm its great potential on improving traffic efficiency, driving safety, and fuel economy.

IEEE Transactions on Control Systems Technology