Navigate like a cabbie: probabilistic reasoning from observed context-aware behavior

  • Authors:
  • Brian D. Ziebart;Andrew L. Maas;Anind K. Dey;J. Andrew Bagnell

  • Affiliations:
  • Carnegie Mellon University, Pittsburgh, PA;Carnegie Mellon University, Pittsburgh, PA;Carnegie Mellon University, Pittsburgh, PA;Carnegie Mellon University, Pittsburgh, PA

  • Venue:
  • UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
  • Year:
  • 2008

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Abstract

We present PROCAB, an efficient method for Probabilistically Reasoning from Observed Context-Aware Behavior. It models the context-dependent utilities and underlying reasons that people take different actions. The model generalizes to unseen situations and scales to incorporate rich contextual information. We train our model using the route preferences of 25 taxi drivers demonstrated in over 100,000 miles of collected data, and demonstrate the performance of our model by inferring: (1) decision at next intersection, (2) route to known destination, and (3) destination given partially traveled route.