  Drivers have unique and rich driving behaviors when operating vehicles in
traffic. This paper presents a novel driver behavior learning approach that
captures the uniqueness and richness of human driver behavior in realistic
driving scenarios. A stochastic inverse reinforcement learning (SIRL) approach
is proposed to learn a distribution of cost function, which represents the
richness of the human driver behavior with a given set of driver-specific
demonstrations. Evaluations are conducted on the realistic driving data
collected from the 3D driver-in-the-loop driving simulation. The results show
that the learned stochastic driver model is capable of expressing the richness
of the human driving strategies under different realistic driving scenarios.
Compared to the deterministic baseline driver behavior model, the results
reveal that the proposed stochastic driver behavior model can better replicate
the driver's unique and rich driving strategies in a variety of traffic
conditions.
