skip to main content
10.1145/3349263.3351327acmconferencesArticle/Chapter ViewAbstractPublication PagesautomotiveuiConference Proceedingsconference-collections
Work in Progress

Improving target selection accuracy for vehicle touch screens

Published: 21 September 2019 Publication History

Abstract

When operating the touch screen in a car, the touch point can shift due to the vibration, resulting in selection errors. Using larger target is a possible solution, but this significantly limits the amount of content that can be displayed on the touch screen. Therefore, we propose a method for in-vehicle touch screen target selection that can be used with a variety of sensors to increase selection accuracy. In this method, the vibration feature is learned by Variational AutoEncoder based model, and it is used for estimating touch point distribution. Our experimental results demonstrate that the proposed method allows users to achieve higher target selection accuracy than conventional methods.

References

[1]
Bashar I. Ahmad, James K. Murphy, Simon J. Godsill, Patrick M. Langdon, and Robert W. Hardy. 2017. Intelligent Intractive Displays in Vehicle with Intent Prediction. IEEE Signal Processing Magazine 34, 2 (2017), 82--94.
[2]
Xiaojun Bi and Shumin Zhai. 2013. Bayesian Touch - A Statistical Criterion of Target Selection with Finger Touch. In Proceedings of the 26th annual ACM symposium on User interface software and technology (UIST). IEEE, 51--60.
[3]
James Bradbury, Stephen Merity, Caiming Xiong, and Richard Socher. 2017. Quasi-Recurrent Neural Networks. In Proceedings of the International Conference on Learning Representations (ICLR).
[4]
Otto Fabius, Joost R, and van Amersfoort. 2015. Variational Recurrent Auto-Encoders. In Proceedings of the International Conference on Learning Representations (ICLR).
[5]
Mayank Goel, Leah Findlater, and Jacob O. Wobbrock. 2012. WalkType: Using Accelerometer Data to Accommodate Situational Impairments in Mobile Touch Screen Text Entry. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI). ACM, 2687--2696.
[6]
Sepp Hochreiter and Jurgen Schmidhuber. 1997. Long Short-Term Memory. Neural computation 9, 8 (1997), 1735--1780.
[7]
Diederik P Kingma and Max Welling. 2013. Auto-Encoding Variational Bayes. In Proceedings of the International Conference on Learning Representations (ICLR).

Index Terms

  1. Improving target selection accuracy for vehicle touch screens

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Conferences
    AutomotiveUI '19: Proceedings of the 11th International Conference on Automotive User Interfaces and Interactive Vehicular Applications: Adjunct Proceedings
    September 2019
    524 pages
    ISBN:9781450369206
    DOI:10.1145/3349263
    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.

    Sponsors

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 21 September 2019

    Check for updates

    Author Tags

    1. finger touch
    2. in-vehicle interaction
    3. machine learning

    Qualifiers

    • Work in progress

    Conference

    AutomotiveUI '19
    Sponsor:

    Acceptance Rates

    Overall Acceptance Rate 248 of 566 submissions, 44%

    Upcoming Conference

    AutomotiveUI '25

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • 0
      Total Citations
    • 170
      Total Downloads
    • Downloads (Last 12 months)17
    • Downloads (Last 6 weeks)0
    Reflects downloads up to 17 Feb 2025

    Other Metrics

    Citations

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Figures

    Tables

    Media

    Share

    Share

    Share this Publication link

    Share on social media