  With the commercial application of automated vehicles (AVs), the sharing of
roads between AVs and human-driven vehicles (HVs) becomes a common occurrence
in the future. While research has focused on improving the safety and
reliability of autonomous driving, it's also crucial to consider collaboration
between AVs and HVs. Human-like interaction is a required capability for AVs,
especially at common unsignalized intersections, as human drivers of HVs expect
to maintain their driving habits for inter-vehicle interactions. This paper
uses the social value orientation (SVO) in the decision-making of vehicles to
describe the social interaction among multiple vehicles. Specifically, we
define the quantitative calculation of the conflict-involved SVO at
unsignalized intersections to enhance decision-making based on the
reinforcement learning method. We use naturalistic driving scenarios with
highly interactive motions for performance evaluation of the proposed method.
Experimental results show that SVO is more effective in characterizing
inter-vehicle interactions than conventional motion state parameters like
velocity, and the proposed method can accurately reproduce naturalistic driving
trajectories compared to behavior cloning.
