In this paper, a multi-agent motion planning problem is studied aiming to minimize energy consumption of connected automated vehicles (CAVs) in lane change scenarios. We model this interactive motion planning as a generalized Nash equilibrium problem and formalize how vehicle-to-vehicle intention sharing enables solution of the game between multiple CAVs as an optimal control problem for each agent, to arrive at a generalized Nash equilibrium. The method is implemented via model predictive control (MPC) and compared with an advanced baseline MPC which utilizes unilateral predictions of other agents' future states. A ROS-based in-the-loop testbed is developed: the method is first evaluated in software-in-the-loop and then vehicle-in-the-loop experiments are conducted. Experimental results demonstrate energy and travel time benefits of the presented method in interactive lane change maneuvers.
@article{arxiv.2411.14567,
title = {Energy Efficient Automated Driving as a GNEP: Vehicle-in-the-loop Experiments},
author = {Viranjan Bhattacharyya and Tyler Ard and Rongyao Wang and Ardalan Vahidi and Yunyi Jia and Jihun Han},
journal= {arXiv preprint arXiv:2411.14567},
year = {2024}
}