  Driving in dense traffic with human and autonomous drivers is a challenging
task that requires high-level planning and reasoning. Human drivers can achieve
this task comfortably, and there has been many efforts to model human driver
strategies. These strategies can be used as inspirations for developing
autonomous driving algorithms or to create high-fidelity simulators.
Reinforcement learning is a common tool to model driver policies, but
conventional training of these models can be computationally expensive and
time-consuming. To address this issue, in this paper, we propose ``skill-based"
hierarchical driving strategies, where motion primitives, i.e. skills, are
designed and used as high-level actions. This reduces the training time for
applications that require multiple models with varying behavior. Simulation
results in a merging scenario demonstrate that the proposed approach yields
driver models that achieve higher performance with less training compared to
baseline reinforcement learning methods.
