Zobrazeno 1 - 10
of 79
pro vyhledávání: '"Tavernini, Davide"'
Autor:
So, Kai Man, Tavolo, Gaetano, Tavernini, Davide, Grosso, Marco, Pozzato, Sergio, Perlo, Pietro, Sorniotti, Aldo
V2X technologies will become widespread in the next generation of passenger cars, and enable the development of novel vehicle control functionalities. Although a wide literature describes the energy efficiency benefits of V2X connectivity, e.g., in t
Externí odkaz:
http://arxiv.org/abs/2406.02211
This study presents a nonlinear model predictive control (NMPC) formulation for preview-based traction control, which uses the information on the expected tire-road friction coefficient ahead to enhance the wheel slip control performance, in the cont
Externí odkaz:
http://arxiv.org/abs/2406.02206
Autor:
Tavolo, Gaetano, Stano, Pietro, Tavernini, Davide, Montanaro, Umberto, Tufo, Manuela, Fiengo, Giovanni, Perlo, Pietro, Sorniotti, Aldo
Path tracking (PT) controllers capable of replicating race driving techniques, such as drifting beyond the limits of handling, have the potential of enhancing active safety in critical conditions. This paper presents a nonlinear model predictive cont
Externí odkaz:
http://arxiv.org/abs/2406.02198
Autor:
Bertipaglia, Alberto, Tavernini, Davide, Montanaro, Umberto, Alirezaei, Mohsen, Happee, Riender, Sorniotti, Aldo, Shyrokau, Barys
This paper presents an original approach to vehicle obstacle avoidance. It involves the development of a nonlinear Model Predictive Contouring Control, which uses torque vectoring to stabilise and drive the vehicle in evasive manoeuvres at the limit
Externí odkaz:
http://arxiv.org/abs/2405.10847
Autonomous agents that drive on roads shared with human drivers must reason about the nuanced interactions among traffic participants. This poses a highly challenging decision making problem since human behavior is influenced by a multitude of factor
Externí odkaz:
http://arxiv.org/abs/2303.16821
To plan safe maneuvers and act with foresight, autonomous vehicles must be capable of accurately predicting the uncertain future. In the context of autonomous driving, deep neural networks have been successfully applied to learning predictive models
Externí odkaz:
http://arxiv.org/abs/2208.00516
Akademický článek
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Autor:
Eichinger, Barbara, Dullnig, Sabine, Perlo, Pietro, Biasiotto, Marco, Romo, Javier, El-Baraka, Khadija, Canitrot, Didier, Faedo, Walter, Baron, Filippo, Skigin, Leonid, Karasikov, Nir, Tavernini, Davide, Sorniotti, Aldo, Catona, Giuseppe, Tanzi, Carlo, Verhulst, Eric
Publikováno v:
In Transportation Research Procedia 2023 72:2848-2855
Autor:
Eichinger, Barbara, Dullnig, Sabine, Perlo, Pietro, Biasiotto, Marco, Garcia, Javier Romo, El-Baraka, Khadija, Canitrot, Didier, Jugovic, Svetislav, Faedo, Walter, Mioni, Andrea, Skigin, Leonid, Karasikov, Nir, So, Kai Man, Tavolo, Gaetano, Tavernini, Davide, Sorniotti, Aldo, Catona, Giuseppe, Tanzi, Carlo, Verhulst, Eric
Publikováno v:
In Transportation Research Procedia 2023 72:2229-2236
Akademický článek
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