CPS: Medium: Emulating Emerging Autonomous Vehicle Technologies to Understand Their Impact on Urban Congestion

CPS：中：模拟新兴自动驾驶汽车技术以了解其对城市拥堵的影响

基本信息

批准号：
1932451
负责人：
Jorge Laval
金额：
$ 75万
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：
2019-10-01 至 2023-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1932451&HistoricalAwards=false
关键词：
CPS Medium Emulating Emerging Autonomous

项目摘要

Self-driving cars are here to stay, and this emerging automated vehicle (AV) technology will transform our transportation system. Potential benefits of AV technology include improved safety and greater capacity for more vehicles to travel on the road (by forming a platoon of vehicles with very close distance with each other). But how AV technologies will evolve in the future is highly uncertain, and so is our understanding of their impacts on our transportation system. For example, none of the AV models in the literature have been validated with empirical data, which makes existing predictions about their impacts highly questionable. Recent studies on a platoon of Tesla vehicles suggest that traffic congestion might actually increase. To address this problem, this project will conduct measurements using commercially available AV vehicles and come up with mathematical models that replicate their behavior. These models will allow us to better understand how AV vehicles behave when they form a platoon with each other and come up with methods to address undesirable consequences such as congestion. The educational component of this project will expose both undergrad and graduate students to a thriving ecosystem where car manufacturers, technology companies and application developers foster innovation via open source software, learning material and data sets to train the machine learning models needed for AV technologies.The research objective of this project is to develop an analytical and numerical framework to emulate the impacts that current AV technologies will have on the transportation networks of the near future. The research approach will be based on the collection of large amounts of empirical data from Level 2/3 AVs currently on the market to train the type of machine learning models that the industry is implementing, consisting of a combination of deep neural networks and expert domain knowledge. Given the recent empirical evidence revealing that these vehicles may exhibit more string instability than human drivers, the project will identify stability constraints that can be incorporated during training to avoid instability. Additionally, the corresponding car-following models that will establish macroscopic dynamics at the network level will be formulated. The project will focus on the longitudinal acceleration/deceleration component since it plays the major role in string stability, network capacity and congestion. It also makes it possible to train machine learning models with a fraction of the data needed for general scenarios, and understanding this simplified driving scenario is the first step towards a successful analysis of more general cases. The impact of this project is expected to be significant as it will establish the connection between machine learning models and car-following models, and will steer research and development of future AV technologies towards artificial intelligence models that are guaranteed to be stable.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.

自动驾驶汽车将留在这里，这种新兴的自动化汽车（AV）技术将改变我们的运输系统。 AV技术的潜在好处包括提高安全性和更大的能力使更多的车辆在道路上行驶（通过形成一排相互距离非常距离的车辆）。但是，未来的AV技术将如何发展是高度不确定的，我们对它们对运输系统的影响的理解也是如此。例如，文献中没有一个通过经验数据验证的AV模型，这对其影响的现有预测高度可疑。关于特斯拉汽车排的最新研究表明，交通拥堵实际上可能会增加。为了解决这个问题，该项目将使用市售的AV车辆进行测量，并提出复制其行为的数学模型。这些模型将使我们能够更好地理解AV车辆相互形成排时的行为，并提出解决不良后果（例如拥塞）的方法。该项目的教育组成部分将使本科生和研究生都曝光到一个蓬勃发展的生态系统，在该系统中，汽车制造商，技术公司和应用程序开发人员通过开源软件，学习材料和数据集培养创新，以培训AV技术所需的机器学习模型。该项目的研究目的是该项目的研究框架，以开发一个分析框架和数字框架，以效仿AVERICTION的不久，这是跨越现有技术的影响。该研究方法将基于目前市场上2/3级AV的大量经验数据的收集，以训练该行业正在实施的机器学习模型的类型，包括深层神经网络和专家领域知识的组合。鉴于最近的经验证据表明，这些车辆可能比人类驾驶员表现出更多的绳子不稳定性，因此该项目将确定可以在训练过程中纳入以避免不稳定的稳定性约束。此外，将制定将在网络级别建立宏观动力学的相应的跟随模型。该项目将集中于纵向加速/减速部分，因为它在弦稳定性，网络容量和拥塞中起着主要作用。它还可以使机器学习模型使用一般方案所需的一小部分数据进行训练，并且了解这种简化的驾驶场景是成功分析更通用情况的第一步。预计该项目的影响将很大，因为它将建立机器学习模型与汽车跟踪模型之间的联系，并将对未来的AV技术进行研究和开发，将未来的AV技术转向人工智能模型，这些模型保证是稳定的。该奖项反映了NSF的法定任务，并通过使用该基金会的知识分子的优点和广泛的影响来评估NSF的法定任务，并被认为是值得的。

项目成果

期刊论文数量（6）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Significance of low-level control to string stability under adaptive cruise control: Algorithms, theory and experiments

DOI：
10.1016/j.trc.2022.103697
发表时间：
2022-07
期刊：
Transportation Research Part C: Emerging Technologies
影响因子：
0
作者：
Hao Zhou;Anye Zhou;Tienan Li;Danjue Chen;S. Peeta;Jorge A. Laval
通讯作者：
Hao Zhou;Anye Zhou;Tienan Li;Danjue Chen;S. Peeta;Jorge A. Laval

Empirical Study on the Acceleration/Deceleration Constraints Under Commercial Adaptive Cruise Control

DOI：
10.1109/itsc55140.2022.9921922
发表时间：
2022-10
期刊：
2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)
影响因子：
0
作者：
Hao Zhou;Anye Zhou;Zijian Ding;Jorge A. Laval;S. Peeta
通讯作者：
Hao Zhou;Anye Zhou;Zijian Ding;Jorge A. Laval;S. Peeta

Car-following behavior characteristics of adaptive cruise control vehicles based on empirical experiments