No need to learn from each other? - Potentials of Knowledge Modeling in Autonomous Vehicle Systems Engineering Towards new methods in multidisciplinary contexts
| Affiliation auteurs | !!!! Error affiliation !!!! |
| Titre | No need to learn from each other? - Potentials of Knowledge Modeling in Autonomous Vehicle Systems Engineering Towards new methods in multidisciplinary contexts |
| Type de publication | Conference Paper |
| Year of Publication | 2017 |
| Auteurs | Schaefer F, Kriesten R, Chrenko D, Ravey A, Gechter F |
| Editor | JardimGoncalves R, Mendonca JP, Pallot M, Zarli A, Martins J, Marques M |
| Conference Name | 2017 INTERNATIONAL CONFERENCE ON ENGINEERING, TECHNOLOGY AND INNOVATION (ICE/ITMC) |
| Publisher | Univ Nova Lisboa, Fac Ciencias Tecnologia; UNINOVA; GRIS; Univ Minho |
| Conference Location | 345 E 47TH ST, NEW YORK, NY 10017 USA |
| ISBN Number | 978-1-5386-0774-9 |
| Mots-clés | Automated Driving (AD), Human Systems Integration (HSI), interdisciplinary engineering, Knowledge Management (KMa), Knowledge Modeling (KMo), Model-Based Systems Engineering (MBSE), multidisciplinary engineering, ontology, Requirements Engineering (RE), Semantic Web (SW), Systems Engineering (SE) |
| Résumé | Engineering autonomous driving functions has become a dramatic challenge in automotive engineering since it is now required to integrate knowledge from multi-disciplinary domains. In this context, the widespread engineering methods are showing their limit since they mainly integrate technological centered point of view. Thus, these new requirements lead naturally to the design of new method for engineering in automotive field. The goal of this paper is to sketch an overview of the possible improvements that Knowledge Modeling and ontologies can bring to Systems Engineering and especially in the case of Autonomous Driving functions. |