Zobrazeno 1 - 10
of 80
pro vyhledávání: '"Bergamini, Luca"'
Despite promising progress in reinforcement learning (RL), developing algorithms for autonomous driving (AD) remains challenging: one of the critical issues being the absence of an open-source platform capable of training and effectively validating t
Externí odkaz:
http://arxiv.org/abs/2111.06889
In this work we are the first to present an offline policy gradient method for learning imitative policies for complex urban driving from a large corpus of real-world demonstrations. This is achieved by building a differentiable data-driven simulator
Externí odkaz:
http://arxiv.org/abs/2109.13333
Autor:
Bergamini, Luca, Ye, Yawei, Scheel, Oliver, Chen, Long, Hu, Chih, Del Pero, Luca, Osinski, Blazej, Grimmett, Hugo, Ondruska, Peter
In this work, we present a simple end-to-end trainable machine learning system capable of realistically simulating driving experiences. This can be used for the verification of self-driving system performance without relying on expensive and time-con
Externí odkaz:
http://arxiv.org/abs/2105.12332
To achieve robustness in Re-Identification, standard methods leverage tracking information in a Video-To-Video fashion. However, these solutions face a large drop in performance for single image queries (e.g., Image-To-Video setting). Recent works ad
Externí odkaz:
http://arxiv.org/abs/2007.04174
In this work we propose a deep learning pipeline to predict the visual future appearance of an urban scene. Despite recent advances, generating the entire scene in an end-to-end fashion is still far from being achieved. Instead, here we follow a two
Externí odkaz:
http://arxiv.org/abs/2007.00323
Autor:
Houston, John, Zuidhof, Guido, Bergamini, Luca, Ye, Yawei, Chen, Long, Jain, Ashesh, Omari, Sammy, Iglovikov, Vladimir, Ondruska, Peter
Motivated by the impact of large-scale datasets on ML systems we present the largest self-driving dataset for motion prediction to date, containing over 1,000 hours of data. This was collected by a fleet of 20 autonomous vehicles along a fixed route
Externí odkaz:
http://arxiv.org/abs/2006.14480
In this work we introduce a new self-supervised, semi-parametric approach for synthesizing novel views of a vehicle starting from a single monocular image. Differently from parametric (i.e. entirely learning-based) methods, we show how a-priori geome
Externí odkaz:
http://arxiv.org/abs/1907.10634
Autor:
Bergamini, Luca, Porrello, Angelo, Dondona, Andrea Capobianco, Del Negro, Ercole, Mattioli, Mauro, D'Alterio, Nicola, Calderara, Simone
People re-identification task has seen enormous improvements in the latest years, mainly due to the development of better image features extraction from deep Convolutional Neural Networks (CNN) and the availability of large datasets. However, little
Externí odkaz:
http://arxiv.org/abs/1902.04886
Autor:
Zippo, Antonio, Iarriccio, Giovanni, Bergamini, Luca, Colombini, Elena, Veronesi, Paolo, Pellicano, Francesco
Publikováno v:
In Journal of Fluids and Structures February 2023 117
Akademický článek
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