Zobrazeno 1 - 10
of 104
pro vyhledávání: '"Oihana Otaegui"'
Publikováno v:
Vehicles, Vol 4, Iss 1, Pp 42-59 (2022)
Local dynamic map (LDM) is a key component in the future of autonomous and connected vehicles. An LDM serves as a local database with the necessary tools to have a common reference system for both static data (i.e., map information) and dynamic data
Externí odkaz:
https://doaj.org/article/bedca0cb7af64ba7b2dbb071ab96ef5a
Autor:
Harbil Arregui, Andoni Mujika, Estibaliz Loyo, Gorka Velez, Michael T. Barros, Oihana Otaegui
Publikováno v:
IEEE Access, Vol 7, Pp 144408-144424 (2019)
Significant efforts have been made and are still being made on short-term traffic prediction methods, especially for highway traffic based on punctual measurements. The literature on predicting the spatial distribution of the traffic in urban interse
Externí odkaz:
https://doaj.org/article/407fb57c200c4192a78fc9c77c42d709
Publikováno v:
SoftwareX, Vol 13, Iss , Pp 100653- (2021)
Data labeling has become a major problem in industries aiming to create and use ground truth labels from massive multi-sensor archives to feed into Artificial Intelligence (AI) applications. Annotation of multi-sensor set-ups with multiple cameras an
Externí odkaz:
https://doaj.org/article/04c6a30ded894891a45e2a6f3f67bd7a
Publikováno v:
Sensors, Vol 22, Iss 7, p 2554 (2022)
Tremendous advances in advanced driver assistance systems (ADAS) have been possible thanks to the emergence of deep neural networks (DNN) and Big Data (BD) technologies. Huge volumes of data can be managed and consumed as training material to create
Externí odkaz:
https://doaj.org/article/7959c8e7f44c40c98f70bc564e4a5c6d
Publikováno v:
Applied Sciences, Vol 11, Iss 17, p 7782 (2021)
Modern Artificial Intelligence (AI) methods can produce a large quantity of accurate and richly described data, in domains such as surveillance or automation. As a result, the need to organize data at a large scale in a semantic structure has arisen
Externí odkaz:
https://doaj.org/article/9474f59565ff46558f166ce5c42de8bf
Autor:
Julian Florez Esnal, Andoni Cortes Vidal, Oihana Otaegui Madurga, Marcos Nieto Doncel, Manuel Graña Romay, Javier Barandiaran Martirena
Publikováno v:
IEEE Transactions on Intelligent Transportation Systems. 23:3115-3125
Lane markings are a key element for Autonomous Driving. The generation of high definition maps and ground-truth data require extensive manual labor. In this paper, we present an efficient and robust method for the offline annotation of lane markings,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6293f18d276ab50cbceeb25f1cfbc970
https://doi.org/10.1109/tits.2020.3031921
https://doi.org/10.1109/tits.2020.3031921
Publikováno v:
Applied Sciences, Vol 10, Iss 12, p 4301 (2020)
An innovative solution named Annotation as a Service (AaaS) has been specifically designed to integrate heterogeneous video annotation workflows into containers and take advantage of a cloud native highly scalable and reliable design based on Kuberne
Externí odkaz:
https://doaj.org/article/964ffeb0632a4d1ab54d9a7b4f2bc378
Autor:
Nerea Aranjuelo, José Luis Apellaniz, Luis Unzueta, Jorge García, Sara García, Oihana Otaegui
Deep Neural Network (DNN)-based vision systems could improve passenger transportation safety by automating processes such as verifying the correct positioning of luggage, seat occupancy, etc. Abundant and well-distributed data are essential to make D
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4b7e95f4895b9851a5df9c477b36be5d
In the frame of 5GMETA, Work Package 1 has the objective of organising the project management activities. The project contains contributions from a number of partners and individual activities requiring close coordination to ensure that Project Miles
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::395c12ee409411997375de381a4363e8
Autor:
Ignacio Arganda-Carreras, Jon Goenetxea, Oihana Otaegui, Unai Elordi, Luis Unzueta, Sergio Sánchez-Carballido
Publikováno v:
IEEE Software. 38:81-87
We provide a novel decomposition methodology from the current MLPerf benchmark to the serverless function execution model. We have tested our approach in Amazon Lambda to benchmark the processing capabilities of OpenCV and OpenVINO inference engines.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::af35142a3f5188f92064cd3dbeb7512d
https://doi.org/10.1109/ms.2020.3030199
https://doi.org/10.1109/ms.2020.3030199