  This paper presents a data-driven Model Predictive Control (MPC) for
energy-efficient urban road driving for connected, automated vehicles. The
proposed MPC aims to minimize total energy consumption by controlling the
vehicle's longitudinal motion on roads with traffic lights and front vehicles.
Its terminal cost function and terminal constraints are learned from data,
which consists of the closed-loop state and input trajectories. The terminal
cost function represents the remaining energy-to-spend starting from a given
terminal state. The terminal constraints are designed to ensure that the
controlled vehicle timely crosses the upcoming traffic light, adheres to
traffic laws, and accounts for the front vehicles. We validate the
effectiveness of our method through both simulations and vehicle-in-the-loop
experiments, demonstrating 19% improvement in average energy efficiency
compared to conventional approaches that involve solving a long-horizon optimal
control problem for speed planning and employing a separate controller for
speed tracking.
