Personalized Comfort Features in Software-defined Vehicles Using Federated Learning

Baran Can Gül, Neeharika Devarakonda, Nasser Jazdi, Michael Weyrich · 2024 · 3 citationsRead the paper

Most of the existing vehicle comfort features operate solely based on user input, lacking consideration for individual preferences, and environmental conditions. This manual adjustment while driving can lead to potential distractions, and jeopardizing user safety. On the other hand, implementing a system where the vehicle control unit learns individual preferences and autonomously adjusts accordingly would significantly enhance the driving experience. In this paper, we examine the thermal comfort of users as one of the comfort features within software-defined vehicles. Given that thermal comfort is influenced by both physiological and external factors, and people have diverse individual preferences, this paper proposes leveraging personalized federated learning to automate and personalize temperature regulation, thereby enhancing thermal comfort of passengers in the vehicle cabin. To validate this concept, we conducted experiments employing a prototype equipped with sensors to collect real-time data, which is then used to train a predictive model. The model's accuracy was assessed using metrics and compared against a centralized approach. In addition, we used a simulator to visualize the potential improvement in thermal comfort with the predicted values. Our findings indicate that temperature control in the vehicle cabin, utilizing federated learning for individualized regulation, outperforms conventional learning approaches, thus yielding significant improvement for thermal comfort of passengers.

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