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

• 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

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.

自动驾驶汽车将继续存在，这种新兴的自动驾驶汽车（AV）技术将改变我们的交通系统。自动驾驶技术的潜在好处包括提高安全性和更大的容量，让更多的车辆在道路上行驶（通过形成一个车队，彼此之间的距离非常近）。 但是，自动驾驶技术在未来将如何发展是高度不确定的，我们对它们对交通系统影响的理解也是如此。例如，文献中的AV模型都没有经过经验数据的验证，这使得现有的关于其影响的预测非常可疑。最近对一排特斯拉汽车的研究表明，交通拥堵实际上可能会增加。为了解决这个问题，该项目将使用商用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了解自动驾驶车辆在相互形成队列时的行为，并提出解决拥堵等不良后果的方法。该项目的教育部分将使本科生和研究生接触到一个蓬勃发展的生态系统，在这个生态系统中，汽车制造商、技术公司和应用程序开发人员通过开源软件促进创新，学习材料和数据集，以训练AV技术所需的机器学习模型。该项目的研究目标是开发一个分析和数值框架，以模拟当前AV技术的影响将在不久的将来对交通网络产生影响。该研究方法将基于从目前市场上的2/3级AV中收集大量经验数据，以训练该行业正在实施的机器学习模型类型，该模型由深度神经网络和专家领域知识组成。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字符串不稳定性，该项目将确定可以在训练过程中纳入的稳定性约束，以避免不稳定。此外，将制定相应的汽车跟随模型，将建立宏观动态网络的水平。该项目将侧重于纵向加速度/减速度分量，因为它在串稳定性、网络容量和拥塞方面起主要作用。它还可以用一般场景所需的一小部分数据来训练机器学习模型，理解这种简化的驾驶场景是成功分析更一般情况的第一步。该项目的影响预计将是重大的，因为它将建立机器学习模型和汽车跟踪模型之间的联系，该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

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
• 作者: Hao Zhou;Anye Zhou;Tienan Li;Danjue Chen;S. Peeta;Jorge A. Laval

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

• DOI: 10.1016/j.trb.2021.03.003
• 发表时间: 2021-05
• 期刊: Transportation Research Part B-methodological
• 影响因子: 6.8
• 作者: Tienan Li;Danjue Chen;Hao Zhou;Jorge A. Laval;Yuanchang Xie

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)
• 作者: Hao Zhou;Anye Zhou;Zijian Ding;Jorge A. Laval;S. Peeta

Congestion-mitigating MPC design for adaptive cruise control based on Newell’s car following model: History outperforms prediction

• DOI: 10.1016/j.trc.2022.103801
• 发表时间: 2022-09
• 期刊: Transportation Research Part C: Emerging Technologies
• 作者: Hao Zhou;Anye Zhou;Tienan Li;Danjue Chen;S. Peeta;Jorge A. Laval

Review of Learning-Based Longitudinal Motion Planning for Autonomous Vehicles: Research Gaps Between Self-Driving and Traffic Congestion

• DOI: 10.1177/03611981211035764
• 发表时间: 2019-10
• 期刊: Transportation Research Record
• 影响因子: 1.7
• 作者: Hao Zhou-;Jorge A. Laval;Anye Zhou;Yu Wang;W. Wu;Zhuo Qing;S. Peeta