A Supervised Learning Concept for Reducing User Interaction in Passenger Cars

TL;DR

This paper presents a supervised learning approach to automate and simplify user interactions in passenger car HMIs, specifically targeting thermal conditioning systems, with potential applications to other setpoint-based interfaces.

Contribution

It introduces a supervised learning method for automating HMI setpoint adjustments, reducing user interaction complexity in passenger cars.

Findings

Effective automation of thermal conditioning setpoints

Reduced user interaction in HMI systems

Potential extension to other setpoint-based HMIs

Abstract

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.