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Predicting opportune moments for in-vehicle proactive speech services

Authors:

Auk Kim,

Woohyeok Choi,

Jungmi Park,

Kyeyoon Kim,

Uichin LeeAuthors Info & Claims

UbiComp/ISWC '19 Adjunct: Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers

Pages 101 - 104

https://doi.org/10.1145/3341162.3343841

Published: 09 September 2019 Publication History

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Abstract

Auditory-verbal or speech interactions with in-vehicle information systems have became increasingly popular. This opens up a whole new realm of possibilities for serving drivers with proactive speech services such as contextualized recommendations and interactive decision-making. However, prior studies have warned that such interactions may consume considerable attentional resources, thus degrade driving performance. This work aims to develop a machine learning model that can find opportune moments for the driver to engage in proactive speech interaction by using the vehicle and environment sensor data. Our machine learning analysis shows that opportune moments for interruption can be conservatively inferred with an accuracy of 0.74.

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Cited By

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Krstačić RŽužić AOrehovački T(2024)Safety Aspects of In-Vehicle Infotainment Systems: A Systematic Literature Review from 2012 to 2023Electronics10.3390/electronics1313256313:13(2563)Online publication date: 29-Jun-2024
https://doi.org/10.3390/electronics13132563
Prajwal MRaj ASen SSaha SGhosh S(2023)Towards Efficient Emotion Self-report Collection Using Human-AI CollaborationProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/35962697:2(1-23)Online publication date: 12-Jun-2023
https://dl.acm.org/doi/10.1145/3596269
Porcheron MClark LNicholson SJones M(2023)Cyclists’ Use of Technology While on Their BikeProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3580971(1-15)Online publication date: 19-Apr-2023
https://dl.acm.org/doi/10.1145/3544548.3580971
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Index Terms

Predicting opportune moments for in-vehicle proactive speech services
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. Interactive systems and tools
      1. User interface management systems
  2. Ubiquitous and mobile computing

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Published In

September 2019

1234 pages

ISBN:9781450368698

DOI:10.1145/3341162

General Chairs:
Robert Harle
Cambridge University
,
Katayoun Farrahi
University Of Southampton
,
Nicholas Lane
University Of Oxford And Samsung Ai

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

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Published: 09 September 2019

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UbiComp '19

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UbiComp '19: The 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing

September 9 - 13, 2019

London, United Kingdom

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Krstačić RŽužić AOrehovački T(2024)Safety Aspects of In-Vehicle Infotainment Systems: A Systematic Literature Review from 2012 to 2023Electronics10.3390/electronics1313256313:13(2563)Online publication date: 29-Jun-2024
https://doi.org/10.3390/electronics13132563
Prajwal MRaj ASen SSaha SGhosh S(2023)Towards Efficient Emotion Self-report Collection Using Human-AI CollaborationProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/35962697:2(1-23)Online publication date: 12-Jun-2023
https://dl.acm.org/doi/10.1145/3596269
Porcheron MClark LNicholson SJones M(2023)Cyclists’ Use of Technology While on Their BikeProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3580971(1-15)Online publication date: 19-Apr-2023
https://dl.acm.org/doi/10.1145/3544548.3580971
Jansen PBritten JHäusele ASegschneider TColley MRukzio E(2023)AutoVis: Enabling Mixed-Immersive Analysis of Automotive User Interface Interaction StudiesProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3580760(1-23)Online publication date: 19-Apr-2023
https://dl.acm.org/doi/10.1145/3544548.3580760
Jansen PColley MRukzio E(2022)A Design Space for Human Sensor and Actuator Focused In-Vehicle Interaction Based on a Systematic Literature ReviewProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/35346176:2(1-51)Online publication date: 7-Jul-2022
https://dl.acm.org/doi/10.1145/3534617

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Interrupting Drivers for Interactions: Predicting Opportune Moments for In-vehicle Proactive Auditory-verbal Tasks

Interruptibility for In-vehicle Multitasking: Influence of Voice Task Demands and Adaptive Behaviors

RFDrive: Tagged Human-Vehicle Interaction for All

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