Collaborative Research: Mixed-Autonomy Traffic Networks: Routing Games and Learning Human Choice Models

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Autonomous and connected vehicles are soon becoming a significant part of roads normally used by human drivers. Such vehicles hold the promise of safer streets, better fuel efficiency, more flexibility in tailoring to specific drivers’ needs, and time savings. However, the appearance of autonomous vehicles driving on roads shared by human-driven cars introduce many interesting and timely challenges. The goal of this proposal is to study (i) traffic networks with mixed autonomy where a fraction of cars are autonomous and the rest are human-driven, and (ii) how humans choose their routes in a traffic network given different options of autonomous service and prices. By studying models of humans’ choices and investigating the characterizations of traffic flow in networks with mixed autonomy, the project develops routing policies to lead the network to an efficient equilibrium with low average latency.This proposal aims to study routing games and human choice models for traffic networks with mixed autonomy. Many studies have shown that mobility can be enhanced in traffic networks such as freeways or signalized intersections when all cars are autonomous; however, such improvement is far from clear for a network with mixed autonomy. The goal of this project is to study the game theory of mixed-autonomy traffic networks and control the autonomous cars’ routing decisions such that the system reaches an optimum equilibrium. Moreover, a novel approach in learning human choices of prices in autonomous transportation services versus latency, or travel time, is developed. Finally, using the well-known fundamental diagram of traffic and cell-transmission model, a dynamic mixed-autonomy traffic model is introduced. Using this dynamic model, we will leverage tools from reinforcement learning to route autonomous cars dynamically and optimally. The proposed research considers both theoretical study of routing games as well as implementation of the developed algorithms in traffic simulators, in particular simulation of Urban Mobility (SUMO). The learned human choice models will also be validated through human subject studies.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.