  This paper proposes a method for modeling human driver interactions that
relies on multi-output gaussian processes. The proposed method is developed as
a refinement of the game theoretical hierarchical reasoning approach called
"level-k reasoning" which conventionally assigns discrete levels of behaviors
to agents. Although it is shown to be an effective modeling tool, the level-k
reasoning approach may pose undesired constraints for predicting human decision
making due to a limited number (usually 2 or 3) of driver policies it extracts.
The proposed approach is put forward to fill this gap in the literature by
introducing a continuous domain framework that enables an infinite policy
space. By using the approach presented in this paper, more accurate driver
models can be obtained, which can then be employed for creating high fidelity
simulation platforms for the validation of autonomous vehicle control
algorithms. The proposed method is validated on a real traffic dataset and
compared with the conventional level-k approach to demonstrate its
contributions and implications.
