Adaptive truck platooning with drones: a decentralized approach for highway monitoring

de Curtò, J. ; Zarzà, I. de ; Cano, Juan Carlos ; Manzoni, Pietro ; Calafate, Carlos T.

Resumen: The increasing demand for efficient and safe transportation systems has led to the development of autonomous vehicles and vehicle platooning. Truck platooning, in particular, offers numerous benefits, such as reduced fuel consumption, enhanced traffic flow, and increased safety. In this paper, we present a drone-based decentralized framework for truck platooning in highway monitoring scenarios. Our approach employs multiple drones, which communicate with the trucks and make real-time decisions on whether to form a platoon or not, leveraging Model Predictive Control (MPC) and Unscented Kalman Filter (UKF) for drone formation control. The proposed framework integrates a simple truck model in the existing drone-based simulation, addressing the truck dynamics and constraints for practical applicability. Simulation results demonstrate the effectiveness of our approach in maintaining the desired platoon formations while ensuring collision avoidance and adhering to the vehicle constraints. This innovative drone-based truck platooning system has the potential to significantly improve highway monitoring efficiency, traffic management, and safety. Our drone-based truck platooning system is primarily designed for implementation in highway monitoring and management scenarios, where its enhanced communication and real-time decision-making capabilities can significantly contribute to traffic efficiency and safety. Future work may focus on field trials to validate the system in real-world conditions and further refine the algorithms based on practical feedback and evolving vehicular technologies.

Idioma: Inglés

DOI: 10.3390/electronics12244913

Año: 2023

Publicado en: Electronics 12, 24 (2023), 4913 [24 pp.]
ISSN: 2079-9292

Factor impacto JCR: 2.6 (2023)

Categ. JCR: COMPUTER SCIENCE, INFORMATION SYSTEMS rank: 115 / 250 = 0.46 (2023) - Q2 - T2
Categ. JCR: PHYSICS, APPLIED rank: 81 / 179 = 0.453 (2023) - Q2 - T2
Categ. JCR: ENGINEERING, ELECTRICAL & ELECTRONIC rank: 157 / 353 = 0.445 (2023) - Q2 - T2

Factor impacto CITESCORE: 5.3 - Hardware and Architecture (Q2) - Control and Systems Engineering (Q2) - Electrical and Electronic Engineering (Q2) - Signal Processing (Q2) - Computer Networks and Communications (Q2)

Factor impacto SCIMAGO: 0.644 - Electrical and Electronic Engineering (Q2) - Computer Networks and Communications (Q2) - Signal Processing (Q2) - Hardware and Architecture (Q2) - Control and Systems Engineering (Q2)

Financiación: info:eu-repo/grantAgreement/ES/MCIN/AEI/PID2021-122580NB-I00

Tipo y forma: Artículo (Versión definitiva)