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

自动驾驶和联网车辆将很快成为人类驾驶员通常使用的道路的重要组成部分。这类车辆有望带来更安全的街道、更高的燃油效率、更灵活地根据特定司机的需求进行定制，并节省时间。然而，自动驾驶汽车在人类驾驶汽车共享的道路上行驶的出现，带来了许多有趣而及时的挑战。本提案的目标是研究(i)混合自治的交通网络，其中一小部分汽车是自动驾驶的，其余的是人类驾驶的，以及（ii）在给定不同的自主服务和价格选择的交通网络中，人类如何选择他们的路线。通过研究人类选择模型和调查混合自治网络中交通流的特征，该项目开发路由策略，使网络达到具有低平均延迟的有效平衡。本文旨在研究混合自治交通网络的路由博弈和人类选择模型。许多研究表明，当所有车辆都是自动驾驶时，可以增强高速公路或信号交叉口等交通网络的移动性；然而，对于一个混合自治的网络来说，这种改善还远未明朗。本课题的目标是研究混合自治交通网络的博弈理论，控制自动驾驶汽车的路径决策，使系统达到最优均衡。此外，还开发了一种新的方法来学习人类对自动运输服务价格与延迟或旅行时间的选择。最后，利用众所周知的交通和蜂窝传输模型的基本图，介绍了一个动态混合自治交通模型。使用这个动态模型，我们将利用强化学习的工具来动态和最佳地安排自动驾驶汽车。提出的研究既考虑了路由博弈的理论研究，也考虑了交通模拟器中开发的算法的实现，特别是城市交通（SUMO）的模拟。学习到的人类选择模型也将通过人类受试者研究得到验证。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.