  Performance evaluation of urban autonomous vehicles requires a realistic
model of the behavior of other road users in the environment. Learning such
models from data involves collecting naturalistic data of real-world human
behavior. In many cases, acquisition of this data can be prohibitively
expensive or intrusive. Additionally, the available data often contain only
typical behaviors and exclude behaviors that are classified as rare events. To
evaluate the performance of AV in such situations, we develop a model of
traffic behavior based on the theory of bounded rationality. Based on the
experiments performed on a large naturalistic driving data, we show that the
developed model can be applied to estimate probability of rare events, as well
as to generate new traffic situations.
