Zobrazeno 1 - 10
of 15
pro vyhledávání: '"Acerbo, Flavia Sofia"'
This paper presents a novel approach to imitation learning from observations, where an autoregressive mixture of experts model is deployed to fit the underlying policy. The parameters of the model are learned via a two-stage framework. By leveraging
Externí odkaz:
http://arxiv.org/abs/2411.08232
This paper proposes DriViDOC: a framework for Driving from Vision through Differentiable Optimal Control, and its application to learn autonomous driving controllers from human demonstrations. DriViDOC combines the automatic inference of relevant fea
Externí odkaz:
http://arxiv.org/abs/2403.15102
This work evaluates and analyzes the combination of imitation learning (IL) and differentiable model predictive control (MPC) for the application of human-like autonomous driving. We combine MPC with a hierarchical learning-based policy, and measure
Externí odkaz:
http://arxiv.org/abs/2211.12111
In recent years, imitation learning (IL) has been widely used in industry as the core of autonomous vehicle (AV) planning modules. However, previous IL works show sample inefficiency and low generalisation in safety-critical scenarios, on which they
Externí odkaz:
http://arxiv.org/abs/2210.01747
To ensure user acceptance of autonomous vehicles (AVs), control systems are being developed to mimic human drivers from demonstrations of desired driving behaviors. Imitation learning (IL) algorithms serve this purpose, but struggle to provide safety
Externí odkaz:
http://arxiv.org/abs/2206.12348
This paper presents a safe imitation learning approach for autonomous vehicle driving, with attention on real-life human driving data and experimental validation. In order to increase occupant's acceptance and gain drivers' trust, the autonomous driv
Externí odkaz:
http://arxiv.org/abs/2110.04052
Publikováno v:
In IFAC PapersOnLine 2023 56(2):2774-2779
Publikováno v:
In IFAC PapersOnLine 2023 56(2):4871-4876
Autor:
Rossi, Claudio, Acerbo, Flavia Sofia, Ylinen, Kaisa, Juga, Ilkka, Nurmi, Pertti, Bosca, Alessio, Tarasconi, Francesco, Cristoforetti, Marco, Alikadic, Azra
Today we are using an unprecedented wealth of social media platforms to generate and share information regarding a wide class of events, which include extreme meteorological conditions and natural hazards such as floods. This paper proposes an automa
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::98b465f0e5c2cae246c240ba4d299934
Autor:
Acerbo, Flavia Sofia, Rossi, Claudio
Publikováno v:
Proceedings of the First CoNEXT Workshop on ICT Tools for Emergency Networks and DisastEr Relief
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=sygma_______::59363d83b60d30158787de4f0074b6b6