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：
2401476
负责人：
Danjue Chen
金额：
$ 18.18万
依托单位：
North Carolina State University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2023
资助国家：
美国
起止时间：
2023-10-01 至 2024-03-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2401476&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.

新兴的自动化车辆（AV）技术可能会破坏和改变我们的运输系统。关于AV的大量研究对AVS对其他车辆的行为方式进行了假设。不幸的是，由于没有AV，很少有任何假设可以在经验上得到验证。然而，对于AV技术的关键组成部分，即自适应巡航控制（ACC）已经使用了十多年，可用于填补这一空白。该研究旨在研究与道路上其他车辆互动时，ACC车辆的行为方式。该研究将对未来的AV行为提供重要的见解。从这项研究中获得的理解也将对ACC车辆和AV的交通管理，运输计划以及设计具有重要意义。此外，该项目将参与一系列综合研究，教育和外展活动，包括与研究和实践社区共享ACC数据，开发教育模块以及通过夏令营的K-12外展。更具体地说，该研究将（i）收集不同汽车制造商及其同行的ACC车辆的经验轨迹数据，常规车辆（RVS）和（ii）制定了捕获ACC和RVS相似和区分特征的追捕汽车模型。该项目将使用最大似然估计（MLE）关注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）的新应用可以测试各种假设，以评估不同ACC系统以及ACC和RV之间的差异和相似性。特别是，该研究将测试来自不同汽车制造商的ACC系统是否相互不同，并且与RVS有所不同，以及它们是否随着时间的推移而发生了很大的变化。知道这一点很重要，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系统最终会汇聚到类似人类的驾驶方面。在建模工作中，研究将建立一个通用的随机模型，以便可以调和ACC车辆和RV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进行模型参数估算。此外，该研究将使用经过测试的ACC的估计模型将结果推送到一般混合流量中。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响审查标准，认为值得通过评估值得支持。