ITSC 2025 Paper Abstract

Paper TH-EA-T24.1

Lui, Dario Giuseppe (Università degli studi di Napoli Federico II), Pane, Gianmarco (University of Naples Federico II), Petrillo, Alberto (University of Naples Federico II), Santini, Stefania (University of Naples Federico II)

Eco-Driving for Uncertain Nonlinear CAEVs Platoon Via a Fully Distributed Adaptive Robust PID-Based Protocol

Scheduled for presentation during the Invited Session "S24b-Traffic Control and Connected Autonomous Vehicles: benefits for efficiency, safety and beyond" (TH-EA-T24), Thursday, November 20, 2025, 13:30−13:50, Coolangata 3

2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), November 18-21, 2025, Gold Coast, Australia

This information is tentative and subject to change. Compiled on October 18, 2025

Keywords Energy-efficient Motion Control for Autonomous Vehicles, Cooperative Driving Systems and Vehicle Coordination in Multi-vehicle Scenarios, Cooperative Vehicle-to-Vehicle Data Sharing for Safe and Efficient Traffic Flow

Abstract

This article addresses the eco-driving control problem for uncertain nonlinear platoons of Connected Autonomous Electric Vehicles. To this aim, a double layer control architecture is exploited for the optimization of the whole platoon energy consumption via the computation of an energy-efficient optimal trajectory which each vehicle has to track. The first layer leverages a Nonlinear Model Predictive Control for the computation of the ecological behavior, while the second layer is embedded via a novel fully distributed adaptive robust PID cooperative driving control. This latter is responsible of enforcing the precise leader-tracking of the followers vehicles, despite the presence of unmodeled dynamics, external disturbance and the lack of knowledge about the communication topology structure. Indeed, the combined action of the PID-like protocol structure and the usage of adaptive gains makes the proposed solution very promptly in counteracting all the uncertain factors affecting the platoon dynamics. Stability analysis, carried out via Lyapunov theory, provides a Linear Matrix Inequality based gains tuning procedure and defines the proper control adaptation mechanism. The effectiveness of the approach is confirmed by virtual simulations in the high-fidelity platform Mixed Traffic Simulator.