Kugele, StefanCebotari, VadimGleirscher, MarioHashemi, MortezaSegler, ChristophShafaei, SinaVögel, Hans-JörgBauer, FridolinKnoll, AloisMarmsoler, DiegoMichel, Hans-UlrichEibl, MaximilianGaedke, Martin2017-08-282017-08-282017978-3-88579-669-5During 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.enAutomotivesafety assuranceservice-oriented architecturescommunicationtimesensitive networkdeep learningmachine learningartificial intelligence10.18420/in2017_1461617-5468