  The integration of Autonomous Vehicles (AVs) into existing human-driven
traffic systems poses considerable challenges, especially within environments
where human and machine interactions are frequent and complex, such as at
unsignalized intersections. To deal with these challenges, we introduce a novel
framework predicated on dynamic and socially-aware decision-making game theory
to augment the social decision-making prowess of AVs in mixed driving
environments. This comprehensive framework is delineated into three primary
modules: Interaction Orientation Identification, Mixed-Strategy Game Modeling,
and Expert Mode Learning. We introduce 'Interaction Orientation' as a metric to
evaluate the social decision-making tendencies of various agents, incorporating
both environmental factors and trajectory characteristics. The mixed-strategy
game model developed as part of this framework considers the evolution of
future traffic scenarios and includes a utility function that balances safety,
operational efficiency, and the unpredictability of environmental conditions.
To adapt to real-world driving complexities, our framework utilizes a dynamic
optimization framework for assimilating and learning from expert human driving
strategies. These strategies are compiled into a comprehensive strategy
library, serving as a reference for future decision-making processes. The
proposed approach is validated through extensive driving datasets and
human-in-loop driving experiments, and the results demonstrate marked
enhancements in decision timing and precision.
