Eco-Driving Control of Connected and Automated Vehicles using Neural
  Network based Rollout
Jacob Paugh Zhaoxuan Zhu Shobhit Gupta Marcello Canova Stephanie Stockar
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.