Navigate Like a Cabbie: Probabilistic Reasoning from Observed Context-Aware Behavior

Brian D. Ziebart, Andrew Maas, J. Andrew Bagnell and Anind K. Dey
International Conference on Ubiquitous Computing (Ubicomp 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.

Related Research Areas:Route prediction, destination prediction, turn prediction, context-aware computing.

Bibtex:

@inproceedings{bziebart-procab,
   author = {Brian D. Ziebart and Andrew Maas 
            and J. Andrew Bagnell and Anind K. Dey},
   title = {Navigate Like a Cabbie: Probabilistic Reasoning from 
           Observed Context-Aware Behavior},
   year = {2008},
   booktitle = {Proc. Ubicomp},
   pages = {322--331}
}

Additional notes:
An "off by one" error in the algorithm originally published is corrected in this version.