Zobrazeno 1 - 10
of 43
pro vyhledávání: '"Llorca, David Fernández"'
Autor:
Martínez, Antonio Hernández, Daza, Iván García, López, Carlos Fernández, Llorca, David Fernández
Accurate vision-based speed estimation is much more cost-effective than traditional methods based on radar or LiDAR. However, it is also challenging due to the limitations of perspective projection on a discrete sensor, as well as the high sensitivit
Externí odkaz:
http://arxiv.org/abs/2407.08380
Autor:
Llorca, David Fernández, Hamon, Ronan, Junklewitz, Henrik, Grosse, Kathrin, Kunze, Lars, Seiniger, Patrick, Swaim, Robert, Reed, Nick, Alahi, Alexandre, Gómez, Emilia, Sánchez, Ignacio, Kriston, Akos
This study explores the complexities of integrating Artificial Intelligence (AI) into Autonomous Vehicles (AVs), examining the challenges introduced by AI components and the impact on testing procedures, focusing on some of the essential requirements
Externí odkaz:
http://arxiv.org/abs/2403.14641
Autor:
Izquierdo, Rubén, Alonso, Javier, Benderius, Ola, Sotelo, Miguel Ángel, Llorca, David Fernández
This study presents the outcomes of empirical investigations pertaining to human-vehicle interactions involving an autonomous vehicle equipped with both internal and external Human Machine Interfaces (HMIs) within a crosswalk scenario. The internal a
Externí odkaz:
http://arxiv.org/abs/2312.07606
This paper addresses the often overlooked issue of fairness in the autonomous driving domain, particularly in vision-based perception and prediction systems, which play a pivotal role in the overall functioning of Autonomous Vehicles (AVs). We focus
Externí odkaz:
http://arxiv.org/abs/2312.06306
Autor:
Serrano, Sergio Martín, Llorca, David Fernández, Daza, Iván García, Vázquez, Miguel Ángel Sotelo
Simulations are gaining increasingly significance in the field of autonomous driving due to the demand for rapid prototyping and extensive testing. Employing physics-based simulation brings several benefits at an affordable cost, while mitigating pot
Externí odkaz:
http://arxiv.org/abs/2309.04418
Autor:
Serrano, Sergio Martín, Izquierdo, Rubén, Daza, Iván García, Sotelo, Miguel Ángel, Llorca, David Fernández
This paper presents the results of tests of interactions between real humans and simulated vehicles in a virtual scenario. Human activity is inserted into the virtual world via a virtual reality interface for pedestrians. The autonomous vehicle is eq
Externí odkaz:
http://arxiv.org/abs/2303.11463
Autor:
Limeros, Sandra Carrasco, Majchrowska, Sylwia, Johnander, Joakim, Petersson, Christoffer, Llorca, David Fernández
Motion prediction systems aim to capture the future behavior of traffic scenarios enabling autonomous vehicles to perform safe and efficient planning. The evolution of these scenarios is highly uncertain and depends on the interactions of agents with
Externí odkaz:
http://arxiv.org/abs/2212.03806
Publikováno v:
Journal of Artificial Intelligence Research, Vol. 76 (2023), pp. 613-644
New emerging technologies powered by Artificial Intelligence (AI) have the potential to disruptively transform our societies for the better. In particular, data-driven learning approaches (i.e., Machine Learning (ML)) have been a true revolution in t
Externí odkaz:
http://arxiv.org/abs/2211.01817
Autor:
Limeros, Sandra Carrasco, Majchrowska, Sylwia, Johnander, Joakim, Petersson, Christoffer, Sotelo, Miguel Ángel, Llorca, David Fernández
Publikováno v:
CAAI Transactions on Intelligence Technology 24686557 (ISSN) 24682322 (eISSN) 2023
Predicting the motion of other road agents enables autonomous vehicles to perform safe and efficient path planning. This task is very complex, as the behaviour of road agents depends on many factors and the number of possible future trajectories can
Externí odkaz:
http://arxiv.org/abs/2210.16144
Autor:
Izquierdo, Rubén, Quintanar, Álvaro, Llorca, David Fernández, Daza, Iván García, Hernández, Noelia, Parra, Ignacio, Sotelo, Miguel Ángel
This work presents a novel method for predicting vehicle trajectories in highway scenarios using efficient bird's eye view representations and convolutional neural networks. Vehicle positions, motion histories, road configuration, and vehicle interac
Externí odkaz:
http://arxiv.org/abs/2207.01407