Emotion-awareness for intelligent vehicle assistants: A research agenda

Hans Jörg Vögel, Christian Sü, Thomas Hubregtsen, Viviane Ghaderi, Ronee Chadowitz, Elisabeth André, Nicholas Cummins, Björn Schuller, Jérme Härri, Raphaël Troncy, Benoit Huet, Melek Nen, Adlen Ksentini, Jörg Conradt, Asaf Adi, Alexander Zadorojniy, Jacques Terken, Jonas Beskow, Ann Morrison, Kynan EngFlorian Eyben, Samer Al Moubayed, Susanne Müller

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

26 Scopus citations

Abstract

EVA1 is describing a new class of emotion-aware autonomous systems delivering intelligent personal assistant functionalities. EVA requires a multi-disciplinary approach, combining a number of critical building blocks into a cybernetics systems/software architecture: emotion aware systems and algorithms, multimodal interaction design, cognitive modelling, decision making and recommender systems, emotion sensing as feedback for learning, and distributed (edge) computing delivering cognitive services.

Original languageEnglish
Title of host publicationProceedings - 2018 ACM/IEEE 1st International Workshop on Software Engineering for AI in Autonomous Systems, SEFAIAS 2018
PublisherIEEE Computer Society
Pages11-15
Number of pages5
ISBN (Electronic)9781450357395
DOIs
StatePublished - 28 May 2018
Externally publishedYes
Event1st ACM/IEEE International Workshop on Software Engineering for AI in Autonomous Systems, SEFAIAS 2018 - Gothenburg, Sweden
Duration: 28 May 2018 → …

Publication series

NameProceedings - International Conference on Software Engineering
ISSN (Print)0270-5257

Conference

Conference1st ACM/IEEE International Workshop on Software Engineering for AI in Autonomous Systems, SEFAIAS 2018
Country/TerritorySweden
CityGothenburg
Period28/05/18 → …

Keywords

  • Cognitive models and proactive recommendations
  • Emotion awareness
  • Emotional state analysis
  • Intelligent assistants
  • IoT
  • Multi-modal interaction design
  • Neuromorphic emotion sensing
  • Privacy preserving machine learning

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