Architectural patterns for cross-domain personalised automotive functions

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.