  Connected and autonomous vehicles have the potential to minimize energy
consumption by optimizing the vehicle velocity and powertrain dynamics with
Vehicle-to-Everything info en route. Existing deterministic and stochastic
methods created to solve the eco-driving problem generally suffer from high
computational and memory requirements, which makes online implementation
challenging.
  This work proposes a hierarchical multi-horizon optimization framework
implemented via a neural network. The neural network learns a full-route value
function to account for the variability in route information and is then used
to approximate the terminal cost in a receding horizon optimization.
Simulations over real-world routes demonstrate that the proposed approach
achieves comparable performance to a stochastic optimization solution obtained
via reinforcement learning, while requiring no sophisticated training paradigm
and negligible on-board memory.
