Maneuver Based Modeling of Driver Decision Making using Game-Theoretic Planning

In this contribution, an approach for modeling driving behavior in intersection scenarios, based on a hybrid dynamic game framework, is presented. Using the hybrid system model, the movement of the traffic agent is divided into maneuvers. Therefore, the decision-making process is a maneuver selection problem having reduced complexity compared to trajectory planning. While previous models used maneuver description with constant acceleration values, the presented approach models the maneuvers on a more macroscopic level using the well known Intelligent Driver Model. This has the advantage of creating more realistic acceleration profiles without increasing the computational complexity of the model. The maneuver selection process is modeled using the nash equilibrium concept of game theory. The resulting coupled optimization problem for each player is solved using an iterated best response algorithm determining the nash equilibrium. Finally, using simulation examples, it is shown that the presented model is capable of creating a variety of scenarios using different parameter sets as well as simulating scenarios with a high number of traffic participants.