No need to learn from each other?

Ferdinand Schaefer , Daniela Chrenko (1) , Franck Gechter (2) , Alexandre Ravey (3, 2) , Reiner Kriesten (4)

1 DRIVE - Département de Recherche en Ingéniérie des Véhicules pour l'Environnement
2 IRTES - SET - Laboratoire Systèmes et Transports
3 FEMTO-ST - Franche-Comté Électronique Mécanique, Thermique et Optique - Sciences et Technologies (UMR 6174)
4 Department of Automotive Engineering for Sustainable Mobility, Karlsruhe university of applied sciences

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.

Domaines: Sciences de l'ingénieur [physics]

Ferdinand Schaefer, Daniela Chrenko, Franck Gechter, Alexandre Ravey, Reiner Kriesten. No need to learn from each other?: Potentials of Knowledge Modeling in Autonomous Vehicle Systems Engineering. International Conference on Engineering, Technology & innocation (ICE/ITMC), Jun 2017, Madère, Portugal. ⟨10.1109/ICE.2017.8279921⟩. ⟨hal-01588160⟩