Peter Stone's Selected Publications

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Inferring User Intention using Gaze in Vehicles

Yu-Sian Jiang, Garrett Warnell, and Peter Stone. Inferring User Intention using Gaze in Vehicles. In The 20th ACM International Conference on Multimodal Interaction (ICMI), October 2018.
Available from AAAI/PAIR and to appear at ICMI

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Abstract

Motivated by the desire to give vehicles better information about their drivers, we explore human intent inference in the setting of a human driver riding in a moving vehicle. Specifically, we consider scenarios in which the driver intends to go to or learn about a specific point of interest along the vehicle's route, and an autonomous system is tasked with inferring this point of interest using gaze cues. Because the scene under observation is highly dynamic --- both the background and objects in the scene move independently relative to the driver --- such scenarios are significantly different from the static scenes considered by most literature in the eye tracking community. In this paper, we provide a formulation for this new problem of determining a point of interest in a dynamic scenario. We design an experimental framework to systematically evaluate initial solutions to this novel problem, and we propose our own solution called \em dynamic interest point detection (DIPD). We experimentally demonstrate the success of DIPD when compared to baseline nearest-neighbor or filtering approaches.

BibTeX Entry

@inproceedings{ICMI18-Jiang,
  title = {Inferring User Intention using Gaze in Vehicles},
  author = {Yu-Sian Jiang and Garrett Warnell and Peter Stone},
  booktitle = {The 20th ACM International Conference on Multimodal Interaction (ICMI)},
  location = {Boulder, Colorado},
  month = {October},
  year = {2018},
  abstract = {
    Motivated by the desire to give vehicles better information about their drivers,
    we explore human intent inference in the setting of a human driver riding in a
      moving vehicle. Specifically, we consider scenarios in which the driver
      intends to go to or learn about a specific point of interest along the vehicle's
      route, and an autonomous system is tasked with inferring this point of interest
      using gaze cues. Because the scene under observation is highly dynamic ---
      both the background and objects in the scene move independently relative to
      the driver --- such scenarios are significantly different from the static scenes
      considered by most literature in the eye tracking community. In this paper, we
      provide a formulation for this new problem of determining a point of interest
      in a dynamic scenario. We design an experimental framework to systematically
      evaluate initial solutions to this novel problem, and we propose our own
      solution called {\em dynamic interest point detection} (DIPD). We
      experimentally demonstrate the success of DIPD when compared to baseline
      nearest-neighbor or filtering approaches.
  },
  wwwnote={Available from <a href="http://www.planrec.org/PAIR/PAIR18/Papers/JiangPair18.pdf">AAAI/PAIR</a> and to appear at <a href="http://icmi.acm.org/2018/">ICMI</a> },
}

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