no code implementations • 25 Mar 2024 • Gianluca D'Amico, Mauro Marinoni, Giorgio Buttazzo
To illustrate the advantages of employing graphic simulation for early-stage testing of perception tasks in the railway domain, this paper presents a comparative analysis of the performance of a SLAM algorithm applied both in a virtual synthetic environment and a real-world scenario.
no code implementations • 28 Feb 2023 • Gianluca D'Amico, Mauro Marinoni, Federico Nesti, Giulio Rossolini, Giorgio Buttazzo, Salvatore Sabina, Gianluigi Lauro
The railway industry is searching for new ways to automate a number of complex train functions, such as object detection, track discrimination, and accurate train positioning, which require the artificial perception of the railway environment through different types of sensors, including cameras, LiDARs, wheel encoders, and inertial measurement units.
1 code implementation • 9 Jun 2022 • Federico Nesti, Giulio Rossolini, Gianluca D'Amico, Alessandro Biondi, Giorgio Buttazzo
Nevertheless, no much work has been devoted to the generation of datasets specifically designed to evaluate the adversarial robustness of neural models.
2 code implementations • 5 Jan 2022 • Giulio Rossolini, Federico Nesti, Gianluca D'Amico, Saasha Nair, Alessandro Biondi, Giorgio Buttazzo
The existence of real-world adversarial examples (commonly in the form of patches) poses a serious threat for the use of deep learning models in safety-critical computer vision tasks such as visual perception in autonomous driving.