  Interacting with other human road users is one of the most challenging tasks
for autonomous vehicles. For congruent driving behaviors, it is essential to
recognize and comprehend sociality, encompassing both implicit social norms and
individualized social preferences of human drivers. To understand and quantify
the complex sociality in driving interactions, we propose a Virtual-Game-based
Interaction Model (VGIM) that is parameterized by a social preference
measurement, Interaction Preference Value (IPV). The IPV is designed to capture
the driver's relative inclination towards individual rewards over group
rewards. A method for identifying IPV from observed driving trajectory is also
developed, with which we assessed human drivers' IPV using driving data
recorded in a typical interactive driving scenario, the unprotected left turn.
Our findings reveal that (1) human drivers exhibit particular social preference
patterns while undertaking specific tasks, such as turning left or proceeding
straight; (2) competitive actions could be strategically conducted by human
drivers in order to coordinate with others. Finally, we discuss the potential
of learning sociality-aware navigation from human demonstrations by
incorporating a rule-based humanlike IPV expressing strategy into VGIM and
optimization-based motion planners. Simulation experiments demonstrate that (1)
IPV identification improves the motion prediction performance in interactive
driving scenarios and (2) the dynamic IPV expressing strategy extracted from
human driving data makes it possible to reproduce humanlike coordination
patterns in the driving interaction.
