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: 1854321
• 负责人: Daniel Work
• 金额: $ 5.76万
• 依托单位: Vanderbilt University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-03-19 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1854321&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.

在接下来的几十年中，自动驾驶汽车将成为高速公路交通流量的组成部分。但是，它们仅构成道路上所有车辆的一小部分。这项研究开发了已经在溪流中采用自动驾驶汽车来改善人类控制车辆的交通流量的技术。目的是减轻不良干扰，交通波，并最终减少燃油消耗。当代对交通流量的控制，例如坡道计量和可变速度限制，在很大程度上仅限于本地和高度聚集的方法。这项研究代表了使用一些自动驾驶汽车迈向全球流量控制的一步，它提供了数学，计算和工程结构，以解决和采用这些新连接。即使自动驾驶汽车只能提供少量减少燃料消耗的比例，由于运输系统对不可再生燃料的严重依赖，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，涉及工程和数学不同学科的人员。它包括对博士生和博士后研究人员的培训，以及向更广泛的公众传播交通研究的活动。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，以使用自动驾驶汽车来检测和减轻对燃料消耗和拥塞的不利影响的交通事件。该方法是将通过自动驾驶汽车以及其他交通数据中的自动驾驶汽车测量的数据与适当的宏观交通模型相结合，以检测和预测拥塞趋势和事件。基于此信息，循环通过经过仔细的规定速度控制器进行了仔细的关闭，这些速度控制器被证明可以减少充血。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油消耗估算的优化方法来确定的。自动驾驶汽车之间的通信以及每个车辆上的计算和控制任务都需​​要解决问题的网络物理方法。这项研究考虑了新型的流量模型（微型麦克罗模型，高阶模型的网络方法），用于交通流量调节的新的控制算法以及由流量中可用的少量可控系统启用的新的感应和控制范式。