A Game-Theoretical Approach to Driving Decision Making in Highway Scenarios

With the development of self-driving technology, the fundamental behaviors like car-following, lane change have been validated and tested in various kinds of scenarios. Currently one of the most challenging domain for self-driving is decision making under dynamic environments. For self-driving cars, it is essential to understand and estimate other vehicles' behavior and behave like a human driver to interact with other vehicles in the mean time. In this paper, a game theoretical approach is proposed to model the interaction of vehicles while considering the surrounding traffic situations. One of the novel move is that a neural network is applied to establish the payoff function in the game which is able to describe the interaction more precisely. The calibration method is then applied to estimate the parameters by using the Next Generation SIMulation (NGSIM) dataset. The experiments demonstrate the accuracy of the proposed method and the ability of making a cooperate decision in highway scenarios.