CARSI II: A Context-Driven Intelligent User Interface


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Date

2024-09

Publication Type

Conference Paper

ETH Bibliography

yes

Citations

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Rights / License

Abstract

Modern automotive infotainment systems offer a complex and wide array of controls and features through various interaction methods. However, such complexity can distract the driver from the primary task of driving, increasing response time and posing safety risks to both car occupants and other road users. Additionally, an overwhelming user interface (UI) can significantly diminish usability and the overall user experience. A simplified UI enhances user experience, reduces driver distraction, and improves road safety. Adaptive UIs that recommend preferred infotainment items to the user represent an intelligent UI, potentially enhancing both user experience and traffic safety. Hence, this paper presents a deep learning foundation model to develop a context-aware recommender system for infotainment systems (CARSI). It can be adopted universally across different user interfaces and car brands, providing a versatile solution for modern infotainment systems. The model demonstrates promising results in identifying driving contexts and providing contextually appropriate UI item recommendations, even for previously unseen users. Furthermore, the model's performance is evaluated with fine-tuning to assess its ability to make personalized recommendations to new users.

Publication status

published

Editor

Book title

AutomotiveUI '24 Adjunct: Adjunct Proceedings of the 16th International Conference on Automotive User Interfaces and Interactive Vehicular Applications

Journal / series

Volume

Pages / Article No.

128 - 135

Publisher

Association for Computing Machinery

Event

16th International Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI 2024)

Edition / version

Methods

Software

Geographic location

Date collected

Date created

Subject

Intelligent user interface; Infotainment system; Context-aware recommender system; Driving context; Deep learning; Transformer; Embedding

Organisational unit

09574 - Frazzoli, Emilio / Frazzoli, Emilio check_circle

Notes

Funding

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