CPS: Synergy: Collaborative Research: Control of Vehicular Traffic Flow via Low Density Autonomous Vehicles

CPS：协同：协作研究：通过低密度自动驾驶车辆控制车流

• 批准号: 1446690
• 负责人: Benjamin Seibold
• 金额: $ 24万
• 依托单位: Temple University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1446690&HistoricalAwards=false
• 关键词: CPS; Synergy; Collaborative; Research; Control

In the next few decades, autonomous vehicles will become an integral part of the traffic flow on highways. However, they will constitute only a small fraction of all vehicles on the road. This research develops technologies to employ autonomous vehicles already in the stream to improve traffic flow of human-controlled vehicles. The goal is to mitigate undesirable jamming, traffic waves, and to ultimately reduce the fuel consumption. Contemporary control of traffic flow, such as ramp metering and variable speed limits, is largely limited to local and highly aggregate approaches. This research represents a step towards global control of traffic using a few autonomous vehicles, and it provides the mathematical, computational, and engineering structure to address and employ these new connections. Even if autonomous vehicles can provide only a small percentage reduction in fuel consumption, this will have a tremendous economic and environmental impact due to the heavy dependence of the transportation system on non-renewable fuels. The project is highly collaborative and interdisciplinary, involving personnel from different disciplines in engineering and mathematics. It includes the training of PhD students and a postdoctoral researcher, and outreach activities to disseminate traffic research to the broader public. This project develops new models, computational methods, software tools, and engineering solutions to employ autonomous vehicles to detect and mitigate traffic events that adversely affect fuel consumption and congestion. The approach is to combine the data measured by autonomous vehicles in the traffic flow, as well as other traffic data, with appropriate macroscopic traffic models to detect and predict congestion trends and events. Based on this information, the loop is closed by carefully following prescribed velocity controllers that are demonstrated to reduce congestion. These controllers require detection and response times that are beyond the limit of a human's ability. The choice of the best control strategy is determined via optimization approaches applied to the multiscale traffic model and suitable fuel consumption estimation. The communication between the autonomous vehicles, combined with the computational and control tasks on each individual vehicle, require a cyber-physical approach to the problem. This research considers new types of traffic models (micro-macro models, network approaches for higher-order models), new control algorithms for traffic flow regulation, and new sensing and control paradigms that are enabled by a small number of controllable systems available in a flow.

在未来几十年，自动驾驶汽车将成为高速公路交通流中不可或缺的一部分。然而，它们只占道路上所有车辆的一小部分。这项研究开发了利用已经存在的自动驾驶汽车来改善人类控制车辆的交通流量的技术。其目标是减轻不希望的堵塞，交通波，并最终减少燃料消耗。当代的交通流控制，如匝道控制和可变限速，在很大程度上局限于本地和高度聚合的方法。这项研究代表了使用一些自动驾驶汽车实现全球交通控制的一步，它提供了数学，计算和工程结构来解决和使用这些新的连接。即使自动驾驶汽车只能减少很小比例的燃料消耗，但由于运输系统严重依赖不可再生燃料，这将产生巨大的经济和环境影响。该项目是高度协作和跨学科的，涉及来自工程和数学不同学科的人员。它包括培训博士生和一名博士后研究员，以及开展外联活动，向更广泛的公众传播交通研究。该项目开发了新的模型、计算方法、软件工具和工程解决方案，以利用自动驾驶汽车来检测和缓解对燃油消耗和拥堵产生不利影响的交通事件。该方法将自动驾驶汽车在交通流中测量的数据以及其他交通数据与适当的宏观交通模型相结合，以检测和预测拥堵趋势和事件。基于此信息，通过仔细遵循规定的速度控制器来闭合回路，所述速度控制器被证明可以减少拥塞。这些控制器需要超出人类能力极限的检测和响应时间。最佳控制策略的选择是通过应用于多尺度交通模型和合适的燃料消耗估计的优化方法来确定的。自动驾驶汽车之间的通信，以及每辆汽车上的计算和控制任务，需要一种网络物理方法来解决这个问题。本研究认为，新类型的交通模型（微观-宏观模型，高阶模型的网络方法），新的控制算法的交通流量调节，和新的传感和控制模式，使少量的可控系统中可用的流。