  This paper presents the design and implementation results of an ecological
adaptive cruise controller (ECO-ACC) which exploits driving automation and
connectivity. The controller avoids front collisions and traffic light
violations, and is designed to reduce the energy consumption of connected
automated vehicles by utilizing historical and real-time signal phase and
timing data of traffic lights that adapt to the current traffic conditions. We
propose an optimization-based generation of a reference velocity, and a
velocity-tracking model predictive controller that avoids front collisions and
violations. We present an experimental setup encompassing the real vehicle and
controller in the loop, and an environment simulator in which the traffic flow
and the traffic light patterns are calibrated on real-world data. We present
and analyze simulation and experimental results, finding a significant
potential for energy consumption reduction, even in the presence of traffic.
