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Collaborative Research: Understanding the Impacts of Automated Vehicles on Traffic Flow Using Empirical Data

合作研究：利用经验数据了解自动驾驶汽车对交通流量的影响

基本信息

批准号：
1826003
负责人：
Jorge Laval
金额：
$ 16.92万
依托单位：
Georgia Tech Research Corporation
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-03-15 至 2022-02-28
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1826003&HistoricalAwards=false
关键词：
Collaborative Research Understanding Impacts Automated

项目摘要

Emerging automated vehicle (AV) technologies are likely to disrupt and transform our transportation system. The vast number of studies on AV hinge upon assumptions on how AVs behave with respect to other vehicles. Unfortunately, few of the assumptions can be empirically validated due to the absence of AVs. And yet, a critical component of AV technologies, the adaptive cruise control (ACC), has been used for over a decade and can be used to fill this gap. The research aims to study how vehicles with ACC behave when interacting with other vehicles on the road. The research will provide important insights on the behaviors of AVs in the future. The understandings gained from this research will also have important implications in traffic management, transportation planning, and design of ACC vehicles and AV. Additionally, this project will engage in a range of integrated research, educational and outreach activities, including sharing the ACC data with the research and practice community, developing educational modules, and K-12 outreach through summer camp. More specifically, the research will (i) collect empirical trajectory data of ACC vehicles of different car-makers and their counterparts, regular vehicles (RVs), and (ii) formulate car-following models that capture the similar and differentiating features of ACCs and RVs. The project will focus on data collection and model estimation efforts using Maximum Likelihood Estimation (MLE). This enables the novel applications of statistical inference methods (e.g., the likelihood ratio test) to test various hypotheses to assess the differences and similarities among different ACC systems and between ACCs and RVs. In particular, the research will test if ACC systems from different car-makers differ from one another and from the RVs, and if they change substantially over time. Knowing this is important because it will dictate whether or not future research in this area has to focus on analyzing each individual carmaker, or if ACC systems will eventually converge towards human-like driving. In the modeling efforts, the research will build a general stochastic model so that the behaviors of ACC vehicles and RVs can be reconciled. The research will examine different variations of the distributions of the model components to specify the model(s) and use MLE for estimation of model parameters. Additionally, the research will use the estimated model(s) of the tested ACCs to extrapolate the results to the general mixed traffic.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.

新兴的自动驾驶汽车 (AV) 技术可能会颠覆和改变我们的交通系统。关于自动驾驶汽车的大量研究都基于对自动驾驶汽车相对于其他车辆的行为方式的假设。不幸的是，由于缺乏自动驾驶，很少有假设能够得到实证验证。然而，自动驾驶技术的关键组成部分自适应巡航控制 (ACC) 已经使用了十多年，可以用来填补这一空白。该研究旨在研究配备 ACC 的车辆在与道路上的其他车辆交互时的行为方式。该研究将为自动驾驶汽车未来的行为提供重要见解。从这项研究中获得的理解也将对交通管理、交通规划以及 ACC 车辆和自动驾驶汽车的设计产生重要影响。此外，该项目还将开展一系列综合研究、教育和推广活动，包括与研究和实践界共享 ACC 数据、开发教育模块以及通过夏令营进行 K-12 推广。更具体地说，该研究将（i）收集不同汽车制造商及其同行、普通车辆（RV）的 ACC 车辆的经验轨迹数据，以及（ii）制定汽车跟随模型，以捕获 ACC 和 RV 的相似和差异特征。该项目将重点关注使用最大似然估计（MLE）的数据收集和模型估计工作。这使得统计推断方法（例如似然比检验）的新颖应用能够测试各种假设，以评估不同 ACC 系统之间以及 ACC 和 RV 之间的差异和相似之处。特别是，该研究将测试不同汽车制造商的 ACC 系统之间以及房车之间是否存在差异，以及它们是否会随着时间的推移而发生重大变化。了解这一点很重要，因为它将决定该领域的未来研究是否必须集中于分析每个汽车制造商，或者 ACC 系统是否最终会趋向于类人驾驶。在建模工作中，该研究将建立一个通用随机模型，以便协调 ACC 车辆和房车的行为。该研究将检查模型组件分布的不同变化，以指定模型并使用 MLE 来估计模型参数。此外，该研究将使用测试的 ACC 的估计模型来推断一般混合交通的结果。该奖项反映了 NSF 的法定使命，并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，被认为值得支持。