145400 20250307094030.0 doi 10.3390/electronics12244913 sideral 140124 ART-2023-140124 eng de Curtò, J. Adaptive truck platooning with drones: a decentralized approach for highway monitoring 2023 Access copy available to the general public Unrestricted 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. info:eu-repo/grantAgreement/ES/MCINN/AEI/PID2021-122580NB-I00 info:eu-repo/semantics/openAccess by http://creativecommons.org/licenses/by/3.0/es/ 2.6 2023 COMPUTER SCIENCE, INFORMATION SYSTEMS 115 / 250 = 0.46 2023 Q2 T2 PHYSICS, APPLIED 81 / 179 = 0.453 2023 Q2 T2 ENGINEERING, ELECTRICAL & ELECTRONIC 157 / 353 = 0.445 2023 Q2 T2 0.644 2023 Computer Networks and Communications 2023 Q2 Control and Systems Engineering 2023 Q2 Signal Processing 2023 Q2 Hardware and Architecture 2023 Q2 Electrical and Electronic Engineering 2023 Q2 5.3 2023 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Zarzà, I. de (orcid)0000-0002-5844-7871 Cano, Juan Carlos Manzoni, Pietro Calafate, Carlos T. 12, 24 (2023), 4913 [24 pp.] Electronics (Basel) Electronics 2079-9292 1229077 http://zaguan.unizar.es/record/145400/files/texto_completo.pdf Versión publicada 2677442 http://zaguan.unizar.es/record/145400/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:145400 articulos driver 2025-03-07-09:38:56 ARTICLE