Eco-Driving Control of Connected and Automated Vehicles using Neural Network based Rollout

Authors:
  • ZZZhaoxuan Zhu
    Zhaoxuan Zhu
  • SGShobhit Gupta
    Shobhit Gupta
  • MCMarcello Canova
    Marcello Canova
  • SSStephanie Stockar
    Stephanie Stockar
Preprint
Publisher website
Google Scholar
Abstract
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.
All Related Versions

This publication has 10 references indexed in Scilit: