  Considering personalized driving preferences, a new decision-making framework
is developed using a differential game approach to resolve the driving
conflicts of autonomous vehicles (AVs) at unsignalized intersections. To
realize human-like driving and personalized decision-making, driving
aggressiveness is first defined for AVs. To improve driving safety, a Gaussian
potential field model is built for collision risk assessment. Besides, in the
proposed decision making framework, the collision risk assessment model is
further used to reduce the computational complexity based on an event-triggered
mechanism. In the construction of payoff function, both driving safety and
passing efficiency are comprehensively considered, and the driving
aggressiveness is also reflected. Two kinds of equilibrium solution to the
differential game, i.e., the Nash equilibrium and Stackelberg equilibrium, are
discussed and solved. Finally, the proposed decision making algorithm is tested
through a hardware-in-the-loop testing platform, and its feasibility,
effectiveness and real-time implementation performance are validated.
