@COMMENT This file was generated by bib2html.pl <https://sourceforge.net/projects/bib2html/> version 0.94
@COMMENT written by Patrick Riley <http://sourceforge.net/users/patstg/>
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@inproceedings{sadigh-aaaifv14,
  author    = {Dorsa Sadigh and Katherine Driggs-Campbell and Alberto Puggelli and Wenchao Li and Victor Shia and Ruzena Bajcsy and Alberto L. Sangiovanni-Vincentelli and S. Shankar Sastry and Sanjit A. Seshia},
  title     = {Data-Driven Probabilistic Modeling and Verification of Human Driver Behavior},
 booktitle = {Formal Verification and Modeling in Human-Machine Systems, AAAI Spring Symposium},
 month = "March",
 year = {2014},
 abstract={We address the problem of formally verifying quantitative 
properties of driver models.We first propose a novel stochastic 
model of the driver behavior based on Convex Markov 
Chains, i.e., Markov chains in which the transition probabilities 
are only known to lie in convex uncertainty sets. This formalism 
captures the intrinsic uncertainty in estimating transition 
probabilities starting from experimentally-collected data. 
We then formally verify properties of the model expressed 
in probabilistic computation tree logic (PCTL). Results show 
that our approach can correctly predict quantitative information 
about driver behavior depending on her state, e.g., 
whether he or she is attentive or distracted.},
}