Dynamic Driving and Routing Games for Autonomous Vehicles on Networks: A Mean Field Game Approach

Abstract

As this era’s biggest game-changer, autonomous vehicles (AV) are expected to exhibit new driving and travel behaviors, thanks to their sensing, communication, and computational capabilities. However, a majority of studies assume AVs are essentially human drivers but react faster, “see” farther, and “know” the road environment better. We believe AVs’ most disruptive characteristic lies in its intelligent goal-seeking and adapting behavior. Building on this understanding, we propose a dynamic game-based control leveraging the notion of mean-field games (MFG). Prof. Di will first introduce how MFG can be applied to the decision-making process of a large number of AVs. To illustrate the potential advantage that AVs may bring to stabilize traffic, she will then introduce a multi-class game where AVs are modeled as intelligent game-players and HVs are modeled using a classical non-equilibrium traffic flow model. Last but not the least, she will talk about how the MFG-based control is generalized to road networks, in which the optimal controls of both velocity and route choice need to be solved for AVs, by resorting to nonlinear complementarity problems.