Toward Contextual and Personalized Interior Experience in a Vehicle: Predictive Preconditioning

摘要

Connected vehicles and other relevant technologies have enabled smart or predictive personalization. However, much personalization is based on explicit driver profile where the driver enters her preferences in the vehicle such as seat heating, seat position or climate control. With the proliferation of big data, cloud computing and IoT, machine learning and AI can be used to learn user preferences implicitly without the need for explicitly setting them. However, the challenge is how to create a system for a personalized smart interior that can provide a proactive and comfortable user experience without inconveniencing the user. In this paper, we address this challenge by creating a machine learning framework for supporting smart interior, and use predictive preconditioning to illustrate this. We implemented this framework in our product BMW Connected and present preliminary results to show that the accuracy is 91%, precision is 76% and recall is 89%. Most active users when receiving the preconditioning notifications, on average, do execute the preconditioning at least twice a week, indicating its usefulness. This framework can be used for personalizing other interior features such as seat heating and seat positioning.