  Accurately modeling the behavior of traffic participants is essential for
safely and efficiently navigating an autonomous vehicle through heavy traffic.
We propose a method, based on the intelligent driver model, that allows us to
accurately model individual driver behaviors from only a small number of frames
using easily observable features. On average, this method makes prediction
errors that have less than 1 meter difference from an oracle with
full-information when analyzed over a 10-second horizon of highway driving. We
then validate the efficiency of our method through extensive analysis against a
competitive data-driven method such as Reinforcement Learning that may be of
independent interest.
