000145400 001__ 145400
000145400 005__ 20241024135331.0
000145400 0247_ $$2doi$$a10.3390/electronics12244913
000145400 0248_ $$2sideral$$a140124
000145400 037__ $$aART-2023-140124
000145400 041__ $$aeng
000145400 100__ $$ade Curtò, J.
000145400 245__ $$aAdaptive truck platooning with drones: a decentralized approach for highway monitoring
000145400 260__ $$c2023
000145400 5060_ $$aAccess copy available to the general public$$fUnrestricted
000145400 5203_ $$aThe 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.
000145400 536__ $$9info:eu-repo/grantAgreement/ES/MCIN/AEI/PID2021-122580NB-I00
000145400 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000145400 590__ $$a2.6$$b2023
000145400 591__ $$aCOMPUTER SCIENCE, INFORMATION SYSTEMS$$b115 / 249 = 0.462$$c2023$$dQ2$$eT2
000145400 591__ $$aPHYSICS, APPLIED$$b81 / 179 = 0.453$$c2023$$dQ2$$eT2
000145400 591__ $$aENGINEERING, ELECTRICAL & ELECTRONIC$$b157 / 352 = 0.446$$c2023$$dQ2$$eT2
000145400 592__ $$a0.644$$b2023
000145400 593__ $$aComputer Networks and Communications$$c2023$$dQ2
000145400 593__ $$aControl and Systems Engineering$$c2023$$dQ2
000145400 593__ $$aSignal Processing$$c2023$$dQ2
000145400 593__ $$aHardware and Architecture$$c2023$$dQ2
000145400 593__ $$aElectrical and Electronic Engineering$$c2023$$dQ2
000145400 594__ $$a5.3$$b2023
000145400 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000145400 700__ $$0(orcid)0000-0002-5844-7871$$aZarzà, I. de
000145400 700__ $$aCano, Juan Carlos
000145400 700__ $$aManzoni, Pietro
000145400 700__ $$aCalafate, Carlos T.
000145400 773__ $$g12, 24 (2023), 4913 [24 pp.]$$pElectronics (Basel)$$tElectronics$$x2079-9292
000145400 8564_ $$s1229077$$uhttps://zaguan.unizar.es/record/145400/files/texto_completo.pdf$$yVersión publicada
000145400 8564_ $$s2677442$$uhttps://zaguan.unizar.es/record/145400/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000145400 909CO $$ooai:zaguan.unizar.es:145400$$particulos$$pdriver
000145400 951__ $$a2024-10-24-12:12:11
000145400 980__ $$aARTICLE