Abstract-Autonomous driving has been the subject of increased interest in recent years both in industry and in academia. Serious efforts are being pursued to address legal, technical, and logistical problems and make autonomous cars a viable option for everyday transportation. One significant challenge is the time and effort required for the verification and validation of the decision and control algorithms employed in these vehicles to ensure a safe and comfortable driving experience. Hundreds of thousands of miles of driving tests are required to achieve a well calibrated control system that is capable of operating an autonomous vehicle in an uncertain traffic environment where interactions among multiple drivers and vehicles occur simultaneously. Traffic simulators where these interactions can be modeled and represented with reasonable fidelity can help to decrease the time and effort necessary for the development of the autonomous driving control algorithms by providing a venue where acceptable initial control calibrations can be achieved quickly and safely before actual road tests. In this paper, we present a game theoretic traffic model that can be used to: 1) test and compare various autonomous vehicle decision and control systems and 2) calibrate the parameters of an existing control system. We demonstrate two example case studies, where, in the first case, we test and quantitatively compare two autonomous vehicle control systems in terms of their safety and performance, and, in the second case, we optimize the parameters of an autonomous vehicle control system, utilizing the proposed traffic model and simulation environment.

This paper studies planar pursuit-evasion games in the presence of obstacles that inhibit the motions of the players. The goal is to construct the dominance regions, where a point in the plane is said to be dominated by one of the players if that player is able to reach the point before the opposing players, regardless of the opposing players' actions. The key achievements of the paper are to provide the dominance regions and to show that an analysis of dominance provides a complete solution to the game. This paper also presents a study of the effects of obstacles by comparing the dominance regions in the presence and absence of obstacles. The obstacles considered include line segments and polygons as well as obstacles that have asymmetric effects on the players. As part of the discussion, a novel, multiplayer pursuit-evasion game is also presented. It features three players on two teams, and it can be used to model rescue scenarios and biological behaviors. The solution of this game cannot be determined from the previous literature, but the methods provided in this paper are used to determine dominance and solve the game.

Abstract-A hierarchical game theoretic decision making framework is exploited to model driver decisions and interactions in traffic. In this paper, we apply this framework to develop a simulator to evaluate various existing autonomous driving algorithms. Specifically, two algorithms, based on Stackelberg policies and decision trees, are quantitatively compared in a traffic scenario where all the human-driven vehicles are modeled using the presented game theoretic approach.

Abstract-This paper describes a game theoretical model of traffic where multiple drivers interact with each other. The model is developed using hierarchical reasoning, a game theoretical model of human behavior, and reinforcement learning. It is assumed that the drivers can observe only a partial state of the traffic they are in and therefore although the environment satisfies the Markov property, it appears as nonMarkovian to the drivers. Hence, each driver implicitly has to find a policy, i.e. a mapping from observations to actions, for a Partially Observable Markov Decision Process. In this paper, a computationally tractable solution to this problem is provided by employing hierarchical reasoning together with a suitable reinforcement learning algorithm. Simulation results are reported, which demonstrate that the resulting driver models provide reasonable behavior for the given traffic scenarios.

No abstract