Architectural patterns for cross-domain personalised automotive functions

Stefan Kugele, Christpoh Segler, Thomas Hubregtsen

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review


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 components. Method: We first formalise a new architectural model. Based on this model, we present and discuss three alternative architectural patterns: (1) concurrent learning, (2) end-to-end learning, and (3) user shadow learning. For these patterns, three alternative integration approaches are discussed: (i) centralised holistic approach, (ii) domain-specific approach, and (iii) dedicated approach. Moreover, we conduct an evaluation using real car data for different customer functions. Conclusion: We propose the use of the user shadow learning pattern in the dynamic architectural model. The user shadow learning pattern is not affected by safety constraints, as is usually the case for integrating artificial intelligence, as it only models user behaviour while leaving the original function intact. To integrate the multitude of models, we propose a domain-specific approach. This approach provides a balance between the trade-offs in the dedicated approach and the holistic approach, being high computational overhead and design complexity, respectively.

Original languageEnglish
Title of host publication2020 IEEE International Conference on Software Architecture (ICSA)
Subtitle of host publicationProceedings
EditorsLisa O' Conner
Place of PublicationPiscataway
Number of pages11
ISBN (Electronic)978-1-7281-4659-1
ISBN (Print)978-1-7281-4660-7
Publication statusPublished - 2020
Event17th IEEE International Conference on Software Architecture, ICSA 2020 - Salvador, Brazil
Duration: 16 Mar 202020 Mar 2020


Conference17th IEEE International Conference on Software Architecture, ICSA 2020


  • Architectural patterns
  • Artificial intelligence
  • Automotive
  • Intelligent functions
  • Personalisation


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