Federated Learning for Comfort Features in Vehicles with Collaborative Sensing: A Review

• Published in: 2024 IEEE 29th International Conference on Emerging Technologies and Factory Automation (ETFA) pp. 1 - 7
• Main Authors: Gul, Baran Can, Dittler, Daniel, Jazdi, Nasser, Weyrich, Michael
• Format: Conference Proceeding
• Language: English
• Published: IEEE 10-09-2024
• Subjects: Automotive engineering; collaborative sensing; Federated learning; Focusing; Human activity recognition; Industries; Manufacturing automation; privacy; Sensors; Technological innovation; Temperature control; Training; vehicle cabin comfort
• ISSN: 1946-0759
• DOI: 10.1109/ETFA61755.2024.10710998

The rapid innovation in the automotive industry highlights the increasing importance of user comfort, especially when integrated with advanced learning scenarios. However, there is a noticeable gap in research focusing on vehicle cabin comfort, particularly in the context of learning and personalization of features. This study conducts a systematic literature review to assess the current state of research in this area. By utilizing federated learning with personalization, a novel and promising technique, various use cases related to vehicle interior comfort are explored. These use cases help derive the requirements needed to address the research question. The methodology of the systematic literature review is detailed, including the evaluation of specific prerequisites. The key finding reveals that no existing study meets all the predefined requirements, underscoring the need for further research in this domain.