Research Challenges for a Future-Proof E/E Architecture - A Project Statement
dc.contributor.author | Kugele, Stefan | |
dc.contributor.author | Cebotari, Vadim | |
dc.contributor.author | Gleirscher, Mario | |
dc.contributor.author | Hashemi, Morteza | |
dc.contributor.author | Segler, Christoph | |
dc.contributor.author | Shafaei, Sina | |
dc.contributor.author | Vögel, Hans-Jörg | |
dc.contributor.author | Bauer, Fridolin | |
dc.contributor.author | Knoll, Alois | |
dc.contributor.author | Marmsoler, Diego | |
dc.contributor.author | Michel, Hans-Ulrich | |
dc.contributor.editor | Eibl, Maximilian | |
dc.contributor.editor | Gaedke, Martin | |
dc.date.accessioned | 2017-08-28T23:47:05Z | |
dc.date.available | 2017-08-28T23:47:05Z | |
dc.date.issued | 2017 | |
dc.description.abstract | During the last decades, the functional power and complexity of automotive E/E architectures grew radically and is going to grow further. We identified two key factors namely autonomy and intelligence. Both pose research challenges for the next generation E/E architecture. We aim to tackle the design challenges with methods and technologies. We propose in this project statement to use a service-oriented architecture on top of an in-vehicle communication network based on time-sensitive networking. Moreover, a rigor risk analysis and mitigation approach enables synthesis of a safety controller. A learning architecture facilitates a shift towards user centralization by proactively adapting functions according to user profiles. In addition, further functions might need to be learned at run-time. | en |
dc.identifier.doi | 10.18420/in2017_146 | |
dc.identifier.isbn | 978-3-88579-669-5 | |
dc.identifier.pissn | 1617-5468 | |
dc.language.iso | en | |
dc.publisher | Gesellschaft für Informatik, Bonn | |
dc.relation.ispartof | INFORMATIK 2017 | |
dc.relation.ispartofseries | Lecture Notes in Informatics (LNI) - Proceedings, Volume P-275 | |
dc.subject | Automotive | |
dc.subject | safety assurance | |
dc.subject | service-oriented architectures | |
dc.subject | communication | |
dc.subject | timesensitive network | |
dc.subject | deep learning | |
dc.subject | machine learning | |
dc.subject | artificial intelligence | |
dc.title | Research Challenges for a Future-Proof E/E Architecture - A Project Statement | en |
gi.citation.endPage | 1474 | |
gi.citation.startPage | 1463 | |
gi.conference.date | 25.-29. September 2017 | |
gi.conference.location | Chemnitz | |
gi.conference.sessiontitle | ASE – 15. Workshop Automotive Software Engineering |
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