A Supervised Learning Concept for Reducing User Interaction in Passenger Cars
This work addresses the challenge of simplifying HMI interactions for car users, but it appears incremental as it applies existing supervised learning methods to a specific domain.
The paper tackles the problem of reducing user interaction complexity in passenger car human-machine interfaces (HMIs) for setpoint adjustment, using supervised learning to achieve automation, with a focus on thermal conditioning systems as an example.
In this article an automation system for human-machine-interfaces (HMI) for setpoint adjustment using supervised learning is presented. We use HMIs of multi-modal thermal conditioning systems in passenger cars as example for a complex setpoint selection system. The goal is the reduction of interaction complexity up to full automation. The approach is not limited to climate control applications but can be extended to other setpoint-based HMIs.