Traffic-Aware Eco-Driving Control in CAVs via Learning-based Terminal Cost Model
Abstract
Connected and Automated Vehicles (CAVs) offer significant potential for improving energy efficiency and lowering vehicle emissions through eco-driving technologies. Control algorithms in CAVs leverage look-ahead route information and Vehicle-to-Everything (V2X) communication to optimize vehicle performance. However, existing eco-driving strategies often neglect macroscopic traffic effects, such as upstream traffic jams, that occur outside the optimization horizon but significantly impact vehicle energy efficiency. This work presents a novel Neural Network (NN)-based methodology to approximate the terminal cost within a model predictive control (MPC) problem framework, explicitly incorporating upstream traffic dynamics. By incorporating traffic jams into the optimization process, the proposed traffic-aware approach yields more energy-efficient speed trajectories compared to traffic-agnostic methods, with minimal impact on travel time. The framework is scalable for real-time implementation while effectively addressing uncertainties from dynamic traffic conditions and macroscopic traffic events.
Cite
@article{arxiv.2510.08980,
title = {Traffic-Aware Eco-Driving Control in CAVs via Learning-based Terminal Cost Model},
author = {Mehmet Fatih Ozkan and Dennis Kibalama and Jacob Paugh and Marcello Canova and Stephanie Stockar},
journal= {arXiv preprint arXiv:2510.08980},
year = {2025}
}