Zobrazeno 1 - 10
of 180
pro vyhledávání: '"Stefania Santini"'
Autor:
Alberto Petrillo, Stefania Santini
Publikováno v:
IEEE Open Journal of Intelligent Transportation Systems, Vol 5, Pp 202-204 (2024)
Mobility is facing a transformation in terms of connectivity, allowing vehicles to communicate with each other, to the road infrastructure, and to other road users. This enables coordination and cooperation, hence managing traffic and mobility at an
Externí odkaz:
https://doaj.org/article/28c3f031b9cb42b49cd8ce2c1228d34d
Double-layer control architecture for motion and torque optimisation of autonomous electric vehicles
Publikováno v:
Transportation Research Interdisciplinary Perspectives, Vol 21, Iss , Pp 100866- (2023)
Optimising energy autonomy for autonomous electric vehicles is a significant challenge in sustainable and environmentally friendly mobility. To this end, we propose a novel double-layer control architecture designed to drive the longitudinal motion o
Externí odkaz:
https://doaj.org/article/d254911e63034b58bdb890ae1490edae
Publikováno v:
IEEE Open Journal of Intelligent Transportation Systems, Vol 4, Pp 481-492 (2023)
This paper addresses the control problem of heterogeneous uncertain nonlinear autonomous vehicle platoons in the presence of adversarial threats arising in Vehicular Ad-hoc NETworks (VANET) during the information sharing process. As unpredictable fau
Externí odkaz:
https://doaj.org/article/b50d53e78a29420bb14320773383d280
Autor:
Dario Giuseppe Lui, Gaetano Tartaglione, Francesco Conti, Gianmaria De Tommasi, Stefania Santini
Publikováno v:
IEEE Access, Vol 11, Pp 30819-30831 (2023)
Due to its extensive applications in different contexts, moving target tracking has become a hot topic in the last years, above all in the military field. Specifically, missile tracking research received a great effort, mainly for its importance in t
Externí odkaz:
https://doaj.org/article/466ecb75f31342e09198604c60f5bf11
Publikováno v:
IEEE Access, Vol 11, Pp 12887-12910 (2023)
The advent of Industry 4.0 has resulted in the widespread usage of novel paradigms and digital technologies within industrial production and manufacturing systems. The objective of making industrial operations monitoring easier also implied the usage
Externí odkaz:
https://doaj.org/article/fc819261a8ed4c1bb3b24d0e730d829f
Publikováno v:
Discover Artificial Intelligence, Vol 2, Iss 1, Pp 1-10 (2022)
Abstract The ever increasing demand in passenger and freight transportation is leading to the saturation of railway network capacity. Virtual Coupling (VC) has been proposed within the European Horizon 2020 Shift2Rail Joint Undertaking as a potential
Externí odkaz:
https://doaj.org/article/76f2fb842c244382892f463f03436e1f
An Optimization Framework for Information Management in Adaptive Automotive Human–Machine Interfaces
Autor:
Francesco Tufano, Sushant Waman Bahadure, Manuela Tufo, Luigi Novella, Giovanni Fiengo, Stefania Santini
Publikováno v:
Applied Sciences, Vol 13, Iss 19, p 10687 (2023)
In recent years, advancements in Intelligent and Connected Vehicles (ICVs) have led to a significant increase in the amount of information to the driver through Human–Machine Interfaces (HMIs). To prevent driver cognitive overload, the development
Externí odkaz:
https://doaj.org/article/eb64284124c047f29847c48b611b1e0e
Autor:
Angelo Candeli, Gianmaria de Tommasi, Dario Giuseppe Lui, Adriano Mele, Stefania Santini, Gaetano Tartaglione
Publikováno v:
IEEE Access, Vol 10, Pp 19685-19696 (2022)
In this paper a Deep Reinforcement Learning algorithm, known as Deep Deterministic Policy Gradient (DDPG), is applied to the problem of designing a missile lateral acceleration control system. To this aim, the autopilot control problem is recast in t
Externí odkaz:
https://doaj.org/article/cd4f2651ae704c9bb7db0e3e417daf08
Publikováno v:
IET Control Theory & Applications, Vol 15, Iss 9, Pp 1169-1184 (2021)
Abstract This paper addresses the time‐varying output formation‐containment control problem for homogeneous high‐order linear Multi‐Agent Systems (MASs). In this context, a PID‐like output‐feedback control strategy is proposed capable to
Externí odkaz:
https://doaj.org/article/57daccafc4f14e6fa9986a74686b67c9
A Hybrid Deep Reinforcement Learning and Optimal Control Architecture for Autonomous Highway Driving
Publikováno v:
Energies, Vol 16, Iss 8, p 3490 (2023)
Autonomous vehicles in highway driving scenarios are expected to become a reality in the next few years. Decision-making and motion planning algorithms, which allow autonomous vehicles to predict and tackle unpredictable road traffic situations, play
Externí odkaz:
https://doaj.org/article/40e2271d2de2406a94bfe9ce99f4c8ba