Driver prediction to improve interaction with in-vehicle HMI

Bret Harsham, Shinji Watanabe, Alan Esenther, John Hershey, Jonathan Le Roux, Yi Luan, Daniel Nikovski, Vamsi Potluru

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

Recently there has been a trend toward increasing the capability of the in-vehicle interface in terms of access to information and complex controls. This has been accompanied by an increase in the complexity of the car Human Machine Interface [HMI], At the same time, studies have shown that driver distraction can contribute to accidents. This paper provides some possible ways to reduce driver cognitive load by augmenting the interface. We use prediction of the driver's next action or intention in order to provide UI affordances for more quickly selecting actions. Two examples of this are presented: prediction of driver interaction with the car HMI based on the driving history, and prediction of driver intention from the driver speech. In the first example, we used signal processing techniques to extract meaningful features from vehicle CAN and history data, and then we used machine learning techniques to predict the driver's next action. In the second example, we used ASR and natural language processing to extract text features from driver speech, and predict user intention using a neural network and word embedding. The proposed prediction methods for user actions and intentions can be used to improve in-vehicle task performance.

Original languageEnglish
Title of host publication7th Biennial Workshop on Digital Signal Processing for In-Vehicle Systems and Safety 2015
PublisherUniversity of Texas at Dallas
Pages1-8
Number of pages8
ISBN (Electronic)9781510827844
Publication statusPublished - 2015 Jan 1
Event7th Biennial Workshop on Digital Signal Processing for In-Vehicle Systems and Safety 2015 - Berkeley, United States
Duration: 2015 Oct 142015 Oct 16

Publication series

Name7th Biennial Workshop on Digital Signal Processing for In-Vehicle Systems and Safety 2015

Other

Other7th Biennial Workshop on Digital Signal Processing for In-Vehicle Systems and Safety 2015
CountryUnited States
CityBerkeley
Period15/10/1415/10/16

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Keywords

  • Car HMI
  • Machine learning
  • Prediction of driver intention
  • Prediction of driver interaction
  • Spoken language understanding

ASJC Scopus subject areas

  • Signal Processing
  • Automotive Engineering
  • Control and Systems Engineering

Cite this

Harsham, B., Watanabe, S., Esenther, A., Hershey, J., Le Roux, J., Luan, Y., Nikovski, D., & Potluru, V. (2015). Driver prediction to improve interaction with in-vehicle HMI. In 7th Biennial Workshop on Digital Signal Processing for In-Vehicle Systems and Safety 2015 (pp. 1-8). (7th Biennial Workshop on Digital Signal Processing for In-Vehicle Systems and Safety 2015). University of Texas at Dallas.