Usability Factors and Guidelines for Climate Control Interfaces Using Big Data from Vehicles

Abstract: Understanding user behavior accurately and designing interactions based on this understanding is essential for providing an enhanced user experience. This study focuses on vehicle climate control systems, aiming to quantitatively analyze user operation frequency through real-time data collected from the vehicles. By examining this behavior, the research seeks to provide actionable insights to optimize interaction design and improve the in-vehicle user experience.VCRM (Vehicle Customer Relationship Management) data was collected from approximately 70,000 vehicles whose drivers subscribed to and consented to connected services. This data is derived from the vehicles’ CAN signals and diagnostic communications, including real user information across various driving environments. The data, linked to details such as driving time, date, and weather conditions, allows for an analysis of macro-level usage patterns over time. For instance, while driving distance did not significantly influence operation patterns, interactions with the climate control system became simpler when driving speeds exceeded 30 km/h. This suggests that drivers tend to rely more on automatic controls at higher speeds, reducing the need for manual adjustments.Based on these insights, this study proposes several guidelines for optimizing climate control system layouts. Frequently used functions should be placed in easily accessible locations, while less commonly used or automated functions can be positioned farther away. This strategy aims to reduce cognitive load while driving and enhance driver safety. Furthermore, the study highlights the importance of customizable interfaces that adapt to different driving conditions and user preferences, allowing for a more personalized experience.In addition to proposing these guidelines, this study developed a concept for integrating them into an IVI (In-Vehicle Infotainment) system. This optimized layout was tested through user experiments, which demonstrated improvements in task completion times and reduced driver distraction. Eye-tracking data particularly revealed that drivers spent less time focusing on the system, thereby lowering cognitive load during operation.By utilizing real driving data instead of traditional surveys or lab-based experiments, this study presents a novel approach to accurately analyze user behavior. This data-driven methodology facilitates the design of more intuitive and user-centered vehicle interfaces, simultaneously improving both safety and user satisfaction. This study underscores the importance of data-driven UX design and offers valuable insights for the future direction of automotive interface design.

Keywords: Usability, UX strategy, data-driven UX, automotive UI

DOI: 10.54941/ahfe1005789

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