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

自动驾驶和联网车辆很快将成为人类驾驶员通常使用的道路的重要组成部分。这种车辆有望使街道更安全，燃油效率更高，更灵活地满足特定驾驶员的需求，并节省时间。然而，自动驾驶车辆在人类驾驶的汽车共享的道路上行驶的出现引入了许多有趣且及时的挑战。该提案的目标是研究（i）具有混合自主性的交通网络，其中一部分汽车是自主的，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项的情况下，人类如何在交通网络中选择路线。通过研究人的选择模型和混合自治网络中交通流的特征，提出了一种使网络在平均延迟较低的情况下达到有效均衡的路由策略，旨在研究混合自治交通网络中的路由博弈和人的选择模型。许多研究表明，当所有汽车都是自主的时，在高速公路或信号交叉口等交通网络中，移动性可以得到增强;然而，对于具有混合自主性的网络，这种改善还远未明确。本计画的目的是研究混合自动化交通网路的博奕理论，并控制自动汽车的路径决策，使系统达到最佳平衡。此外，开发了一种新的方法来学习人类在自主运输服务中对延迟或旅行时间的价格选择。最后，利用著名的流量基本图和小区传输模型，引入了动态混合自治流量模型。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路由自动汽车。建议的研究既考虑了路由游戏的理论研究，也考虑了在交通模拟器中实现所开发的算法，特别是城市移动性模拟（SUMO）。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。

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.