Zobrazeno 1 - 10
of 52
pro vyhledávání: '"Michael Botsch"'
Autor:
Alberto Flores Fernández, Eduardo Sánchez Morales, Michael Botsch, Christian Facchi, Andrés García Higuera
Publikováno v:
Sensors, Vol 23, Iss 1, p 159 (2022)
A highly accurate reference vehicle state is a requisite for the evaluation and validation of Autonomous Driving (AD) and Advanced Driver Assistance Systems (ADASs). This highly accurate vehicle state is usually obtained by means of Inertial Navigati
Externí odkaz:
https://doaj.org/article/972545014ee44cd0b440ce92a5cfe7a9
Autor:
Alberto Flores Fernández, Jonas Wurst, Eduardo Sánchez Morales, Michael Botsch, Christian Facchi, Andrés García Higuera
Publikováno v:
Sensors, Vol 22, Iss 12, p 4498 (2022)
The prediction of the motion of traffic participants is a crucial aspect for the research and development of Automated Driving Systems (ADSs). Recent approaches are based on multi-modal motion prediction, which requires the assignment of a probabilit
Externí odkaz:
https://doaj.org/article/ba3c3e2cf3f046a4909dab0370763159
Publikováno v:
Sensors, Vol 21, Iss 4, p 1131 (2021)
A current trend in automotive research is autonomous driving. For the proper testing and validation of automated driving functions a reference vehicle state is required. Global Navigation Satellite Systems (GNSS) are useful in the automation of the v
Externí odkaz:
https://doaj.org/article/cda5947b1d4943e8b900903ac23250c7
Publikováno v:
IEEE Transactions on Intelligent Vehicles. 8:3506-3521
Publikováno v:
2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC).
Autor:
Wolfgang Utschick, Michael Botsch
Publikováno v:
Fahrzeugsicherheit und automatisiertes Fahren. :1-8
Publikováno v:
ITSC
This paper proposes an interpretable machine learning structure for the early prediction of lane changes. The interpretability relies on interpretable templates, as well as constrained weights during the training process of a neural network. It is sh
Traffic scenario categorisation is an essential component of automated driving, for e.\,g., in motion planning algorithms and their validation. Finding new relevant scenarios without handcrafted steps reduce the required resources for the development
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::566267b9ca39759b64f1a9081e180e35
http://arxiv.org/abs/2105.07639
http://arxiv.org/abs/2105.07639
Autor:
Michael Koller, Michael Botsch, Oliver De Candido, Ron Melz, Oliver Gallitz, Wolfgang Utschick
Publikováno v:
ITSC
In this paper, we explore different Convolutional Neural Network (CNN) architectures to extract features in a Time to Lane Change (TTLC) classification problem for highway driving functions. These networks are trained using the HighD dataset, a publi