activity
20162023
most citedOn the Forces of Driver Distraction: Explainable Predictions for the Visual Demand of In-Vehicle Touchscreen Interactions

54 citations · 299 across the 34 of their papers we have counts for

collaborators

41 papers

cs.HC2023★ 3 cited

Exploring Millions of User Interactions with ICEBOAT: Big Data Analytics for Automotive User Interfaces

Patrick Ebel, Kim Julian Gülle, Christoph Lingenfelder +1

User Experience (UX) professionals need to be able to analyze large amounts of usage data on their own to make evidence-based design decisions. However, the design process for In-V…

cs.SE2023

Explanation Needs in App Reviews: Taxonomy and Automated Detection

Max Unterbusch, Mersedeh Sadeghi, Jannik Fischbach +2

Explainability, i.e. the ability of a system to explain its behavior to users, has become an important quality of software-intensive systems. Recent work has focused on methods for…

cs.HC2023★ 20 cited

Multitasking while Driving: How Drivers Self-Regulate their Interaction with In-Vehicle Touchscreens in Automated Driving

Patrick Ebel, Christoph Lingenfelder, Andreas Vogelsang

Driver assistance systems are designed to increase comfort and safety by automating parts of the driving task. At the same time, modern in-vehicle information systems with large to…

cs.SE2023

Automatically Classifying Kano Model Factors in App Reviews

Michelle Binder, Annika Vogt, Adrian Bajraktari +1

[Context and motivation] Requirements assessment by means of the Kano model is common practice. As suggested by the original authors, these assessments are done by interviewing sta…

cs.HC2023★ 54 cited

On the Forces of Driver Distraction: Explainable Predictions for the Visual Demand of In-Vehicle Touchscreen Interactions

Patrick Ebel, Christoph Lingenfelder, Andreas Vogelsang

With modern infotainment systems, drivers are increasingly tempted to engage in secondary tasks while driving. Since distracted driving is already one of the main causes of fatal a…

cs.HC2022★ 4 cited

ICEBOAT: An Interactive User Behavior Analysis Tool for Automotive User Interfaces

Patrick Ebel, Kim Julian Gülle, Christoph Lingenfelder +1

In this work, we present ICEBOAT an interactive tool that enables automotive UX experts to explore how users interact with In-vehicle Information Systems. Based on large naturalist…