  Connected and Automated Vehicles (CAVs), in particular those with multiple
power sources, have the potential to significantly reduce fuel consumption and
travel time in real-world driving conditions. In particular, the Eco-driving
problem seeks to design optimal speed and power usage profiles based upon
look-ahead information from connectivity and advanced mapping features, to
minimize the fuel consumption over a given itinerary.
  In this work, the Eco-driving problem is formulated as a Partially Observable
Markov Decision Process (POMDP), which is then solved with a state-of-art Deep
Reinforcement Learning (DRL) Actor Critic algorithm, Proximal Policy
Optimization. An Eco-driving simulation environment is developed for training
and evaluation purposes. To benchmark the performance of the DRL controller, a
baseline controller representing the human driver, a trajectory optimization
algorithm and the wait-and-see deterministic optimal solution are presented.
With a minimal onboard computational requirement and a comparable travel time,
the DRL controller reduces the fuel consumption by more than 17% compared
against the baseline controller by modulating the vehicle velocity over the
route and performing energy-efficient approach and departure at signalized
intersections, over-performing the more computationally demanding trajectory
optimization method
