Visualizing natural language interaction for conversational in-vehicle information systems to minimize driver distraction

Michael Braun (Corresponding author), Nora Broy, Bastian Pfleging, Florian Alt

Research output: Contribution to journalArticleAcademicpeer-review

2 Citations (Scopus)


In this paper we investigate how natural language interfaces can be integrated with cars in a way such that their influence on driving performance is being minimized. In particular, we focus on how speech-based interaction can be supported through a visualization of the conversation. Our work is motivated by the fact that speech interfaces (like Alexa, Siri, Cortana, etc.) are increasingly finding their way into our everyday life. We expect such interfaces to become commonplace in vehicles in the future. Cars are a challenging environment, since speech interaction here is a secondary task that should not negatively affect the primary task, that is driving. At the outset of our work, we identify the design space for such interfaces. We then compare different visualization concepts in a driving simulator study with 64 participants. Our results yield that (1) text summaries support drivers in recalling information and enhances user experience but can also increase distraction, (2) the use of keywords minimizes cognitive load and influence on driving performance, and (3) the use of icons increases the attractiveness of the interface.

Original languageEnglish
Pages (from-to)71-88
Number of pages18
JournalJournal on Multimodal User Interfaces
Issue number2
Early online date19 Mar 2019
Publication statusPublished - Jun 2019



  • Automotive user interfaces
  • Human–computer interaction
  • Natural language interfaces

Cite this