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of 10
pro vyhledávání: '"Christoph Segler"'
Publikováno v:
ICSA
Context: Future automotive customer functions will be highly personalisable and adapt their settings proactively in an intelligent way. Aim: We aim at designing generic architectural patterns for functional architectures containing machine learning c
Publikováno v:
ITSC
Context: With the growing prevalence of machine learning applications within the automotive domain, we observe an increase of built-in sensors and generated data. This data includes complex data such as point clouds for the environmental model but al
Autor:
Christoph Segler, Josef Pichlmeier, Koen Bertels, Aritra Sarkar, Thomas Hubregtsen, Thomas Gabor
Publikováno v:
ISQED
Quantum computers hold great promise for accelerating computationally challenging algorithms on noisy intermediate-scale quantum (NISQ) devices in the upcoming years. Much attention of the current research is directed to algorithmic research on artif
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9238243667d0f17216164326a367d3a7
Autor:
Eric Sax, Christoph Segler, Sina Shafaci, Mohd Hafeez Osman, Alois Knoll, Philipp Obergfell, Stefan Kugele
Publikováno v:
IV
Context: As we move towards higher levels of automation in autonomous driving, we see an increase in functionality that either assists or takes over in both normal and emergency scenarios. These new functionalities can be intentionally switched off b
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0fef31d3c6e992da1e743b1e8be2e902
https://mediatum.ub.tum.de/doc/1483918/document.pdf
https://mediatum.ub.tum.de/doc/1483918/document.pdf
Publikováno v:
ICSA Companion
One major challenge in the automotive industry is to deliver innovative functions more frequently. Hence, the standard development process with a fixed release plan is likely to be turned into a more continuous procedure. From a methodological perspe
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9e30b41d5729bb75a5ad0de360ceb2de
https://mediatum.ub.tum.de/doc/1486873/document.pdf
https://mediatum.ub.tum.de/doc/1486873/document.pdf
Autor:
Christoph Segler, Mohd Hafeez Osman, Philipp Obergfell, Sina Shafaei, Alois Knoll, Stefan Kugele, Eric Sax
Publikováno v:
ICSE (Companion Volume)
As we move towards higher levels of automation in autonomous driving, we see an increase in functionality that either assists or takes over in both normal and emergency scenarios. These new functionalities can be switched off by the user for personal
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::25feea3326bcf5c70e7298f433e9c361
https://mediatum.ub.tum.de/doc/1506725/document.pdf
https://mediatum.ub.tum.de/doc/1506725/document.pdf
Publikováno v:
ASE
More and more software-intensive systems employ machine learning and runtime optimization to improve their functionality by providing advanced features (e. g. personal driving assistants or recommendation engines). Such systems incorporate a number o
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b9836c6c5b78679f9f0dc8bc70247873
https://mediatum.ub.tum.de/1518151
https://mediatum.ub.tum.de/1518151
Publikováno v:
2018 IEEE International Systems Engineering Symposium (ISSE).
In contrast to current automotive system architectures, future architectures will continuously gain knowledge at run-time with the help of machine learning techniques. For making this knowledge visible to the developer, synchronization mechanisms bet
Publikováno v:
Advances in Intelligent Systems and Computing ISBN: 9783319604404
In near future, several complex automation modes – like SAE-level 2 and 3 – may be employed in one vehicle. In order to investigate the relevance of mode awareness and mode errors in the context of vehicle automation, a study with 49 participants
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::218d4b0195bb79a2eb0ace5464e83636
https://doi.org/10.1007/978-3-319-60441-1_70
https://doi.org/10.1007/978-3-319-60441-1_70