  Driver models are invaluable for planning in autonomous vehicles as well as
validating their safety in simulation. Highly parameterized black-box driver
models are very expressive, and can capture nuanced behavior. However, they
usually lack interpretability and sometimes exhibit unrealistic-even
dangerous-behavior. Rule-based models are interpretable, and can be designed to
guarantee "safe" behavior, but are less expressive due to their low number of
parameters. In this article, we show that online parameter estimation applied
to the Intelligent Driver Model captures nuanced individual driving behavior
while providing collision free trajectories. We solve the online parameter
estimation problem using particle filtering, and benchmark performance against
rule-based and black-box driver models on two real world driving data sets. We
evaluate the closeness of our driver model to ground truth data demonstration
and also assess the safety of the resulting emergent driving behavior.
