  In this paper we investigate the effect of the unpredictability of
surrounding cars on an ego-car performing a driving maneuver. We use Maximum
Entropy Inverse Reinforcement Learning to model reward functions for an ego-car
conducting a lane change in a highway setting. We define a new feature based on
the unpredictability of surrounding cars and use it in the reward function. We
learn two reward functions from human data: a baseline and one that
incorporates our defined unpredictability feature, then compare their
performance with a quantitative and qualitative evaluation. Our evaluation
demonstrates that incorporating the unpredictability feature leads to a better
fit of human-generated test data. These results encourage further investigation
of the effect of unpredictability on driving behavior.
