Search Results for author: Alessandro Toschi

Found 5 papers, 0 papers with code

A Tricycle Model to Accurately Control an Autonomous Racecar with Locked Differential

no code implementations22 Dec 2023 Ayoub Raji, Nicola Musiu, Alessandro Toschi, Francesco Prignoli, Eugenio Mascaro, Pietro Musso, Francesco Amerotti, Alexander Liniger, Silvio Sorrentino, Marko Bertogna

In this paper, we present a novel formulation to model the effects of a locked differential on the lateral dynamics of an autonomous open-wheel racecar.

er.autopilot 1.0: The Full Autonomous Stack for Oval Racing at High Speeds

no code implementations27 Oct 2023 Ayoub Raji, Danilo Caporale, Francesco Gatti, Andrea Giove, Micaela Verucchi, Davide Malatesta, Nicola Musiu, Alessandro Toschi, Silviu Roberto Popitanu, Fabio Bagni, Massimiliano Bosi, Alexander Liniger, Marko Bertogna, Daniele Morra, Francesco Amerotti, Luca Bartoli, Federico Martello, Riccardo Porta

The Indy Autonomous Challenge (IAC) brought together for the first time in history nine autonomous racing teams competing at unprecedented speed and in head-to-head scenario, using independently developed software on open-wheel racecars.

Motion Planning and Control for Multi Vehicle Autonomous Racing at High Speeds

no code implementations22 Jul 2022 Ayoub Raji, Alexander Liniger, Andrea Giove, Alessandro Toschi, Nicola Musiu, Daniele Morra, Micaela Verucchi, Danilo Caporale, Marko Bertogna

This paper presents a multi-layer motion planning and control architecture for autonomous racing, capable of avoiding static obstacles, performing active overtakes, and reaching velocities above 75 $m/s$.

Motion Planning

Fast and Accurate Error Simulation for CNNs against Soft Errors

no code implementations4 Jun 2022 Cristiana Bolchini, Luca Cassano, Antonio Miele, Alessandro Toschi

We compared our methodology against SASSIFI for the accuracy of functional error simulation w. r. t.

The dynamical vertex approximation for many-electron systems with spontaneously broken SU(2)-symmetry

no code implementations8 Nov 2020 Lorenzo Del Re, Alessandro Toschi

We generalize the formalism of the dynamical vertex approximation (D$\Gamma$A) -- a diagrammatic extension of the dynamical mean-field theory (DMFT)-- to treat magnetically ordered phases.

Strongly Correlated Electrons Quantum Gases

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