  Autonomous vehicles need to model the behavior of surrounding human driven
vehicles to be safe and efficient traffic participants. Existing approaches to
modeling human driving behavior have relied on both data-driven and rule-based
methods. While data-driven models are more expressive, rule-based models are
interpretable, which is an important requirement for safety-critical domains
like driving. However, rule-based models are not sufficiently representative of
data, and data-driven models are yet unable to generate realistic traffic
simulation due to unrealistic driving behavior such as collisions. In this
paper, we propose a methodology that combines rule-based modeling with
data-driven learning. While the rules are governed by interpretable parameters
of the driver model, these parameters are learned online from driving
demonstration data using particle filtering. We perform driver modeling
experiments on the task of highway driving and merging using data from three
real-world driving demonstration datasets. Our results show that driver models
based on our hybrid rule-based and data-driven approach can accurately capture
real-world driving behavior. Further, we assess the realism of the driving
behavior generated by our model by having humans perform a driving Turing test,
where they are asked to distinguish between videos of real driving and those
generated using our driver models.
