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

arXiv: https://arxiv.org/abs/2310.10878 · HTML: https://mlsanity.com/paper/2310.10878/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Cooperative and V2X driving, one of 9 directions in End-to-end autonomous driving.
- Builds on 3 of its 14 references that are in the map.
- First posted on arXiv 16 Oct 2023.

## Placed in

- End-to-end autonomous driving / [Cooperative and V2X driving](https://mlsanity.com/driving/cooperative-and-v2x-driving/)

## Builds on (most cited first, 3 of 3)

- [Safe Model-based Off-policy Reinforcement Learning for Eco-Driving in Connected and Automated Hybrid Electric Vehicles](https://mlsanity.com/paper/2105.11640/) (2021)
- [A GPU Implementation of a Look-Ahead Optimal Controller for Eco-Driving Based on Dynamic Programming](https://mlsanity.com/paper/2104.01284/) (2021)
- [Sequential Optimization of Speed, Thermal Load, and Power Split in Connected HEVs](https://mlsanity.com/paper/1903.08561/) (2019)

## Abstract (arXiv)

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.