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Collaborative Research: Learning-Based Scalable Predictive Control Strategies for Heterogeneous Traffic Networks

协作研究：异构交通网络基于学习的可扩展预测控制策略

基本信息

批准号：
2130718
负责人：
Baisravan HomChaudhuri
金额：
$ 21.21万
依托单位：
Illinois Institute of Technology
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2022
资助国家：
美国
起止时间：
2022-01-01 至 2024-12-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2130718&HistoricalAwards=false
关键词：
Collaborative Research Learning Based Scalable

项目摘要

The widespread adoption of connected and automated vehicle technology is likely to take place over a number of years as the technology becomes more commonly accepted by the public and approved by regulatory authorities. Until then, it is essential to develop traffic management strategies that consider the uncertainty associated with heterogeneities in traffic networks and understand the extent to which these strategies improve the performance of traffic networks. This research project aims to develop and validate infrastructure- and vehicle-based control strategies to enhance heterogeneous traffic networks, addressing human-driven and automated vehicles, mobility, and energy efficiency. The project outcomes will be of interest to municipalities and transportation agencies, the automotive industry, and equipment manufacturers. Specifically, the control approaches will be of value to transportation agencies in understanding how infrastructure-based strategies can be exploited to improve energy efficiency and mobility in mixed traffic environments. Real-time control algorithms developed for autonomous vehicles can help the automotive industry determine a set of protocols that address the needs for safe and effective navigation in a mixed traffic network. Further, the models and techniques developed in this research are expected to have implications for a wide range of applications where the system's behavior can be modeled as an uncertain heterogeneous system, such as aerial and ground mobile robots operating in search and rescue missions. The educational plan is designed to impact graduate and undergraduate students, K-12 students, and minority students to prepare and engage a diverse STEM workforce.This collaborative research aims to develop a framework for tractable modeling and optimal control of a heterogeneous traffic network consisting of autonomous and human-driven vehicles. This goal will be realized by combining data-driven modeling of uncertain systems, stochastic model predictive control, and distributed optimization. The project defines three research objectives: (1) development of distributed learning- and scenario-based model predictive control methods at the upper (macroscopic) level wherein functional variational Bayesian neural networks will be used to model the state- and input-dependent uncertainty associated with the heterogeneity in the traffic network, and distributed optimization algorithms will be used to enhance the computational efficiencies of the proposed control approach; (2) development of distributed cautious model predictive control-based approaches for heterogeneous multi-agent systems at the lower (microscopic) level to ensure the safety of individual vehicles while tracking the desired reference command set by the macroscopic-level controller; (3) test the effectiveness of the hierarchical learning-based control paradigm for both urban and highway traffic networks using the PTV-VISSIM traffic simulation software.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.

联网和自动驾驶汽车技术的广泛采用可能需要数年时间，因为该技术越来越被公众接受并得到监管机构的批准。在此之前，它是必不可少的，以制定交通管理策略，考虑在交通网络中的异质性相关的不确定性，并了解这些策略在何种程度上提高交通网络的性能。该研究项目旨在开发和验证基于基础设施和车辆的控制策略，以增强异构交通网络，解决人类驾驶和自动驾驶车辆，移动性和能源效率问题。项目成果将引起市政当局和运输机构、汽车行业和设备制造商的兴趣。具体而言，控制方法将是有价值的运输机构在了解如何利用基础设施为基础的战略，以提高能源效率和流动性的混合交通环境。为自动驾驶汽车开发的实时控制算法可以帮助汽车行业确定一套协议，以满足混合交通网络中安全有效的导航需求。此外，在这项研究中开发的模型和技术，预计将有广泛的应用，系统的行为可以建模为一个不确定的异构系统，如空中和地面移动的机器人在搜索和救援任务的影响。该教育计划旨在影响研究生和本科生，K-12学生和少数民族学生，以准备和参与多元化的STEM劳动力。这项合作研究旨在开发一个框架，用于由自动驾驶和人类驾驶车辆组成的异构交通网络的易处理建模和优化控制。这一目标将通过结合不确定系统的数据驱动建模、随机模型预测控制和分布式优化来实现。该项目确定了三个研究目标：（1）开发基于分布式学习和基于神经网络的模型预测控制方法，（宏观）水平，其中函数变分贝叶斯神经网络将用于对与交通网络中的异质性相关联的状态和输入依赖的不确定性进行建模，分布式优化算法将用于提高所提出的控制方法的计算效率;（2）研究了基于分布式谨慎模型预测控制的异构多智能体系统的控制方法，（微观）级，以确保单个车辆的安全，同时跟踪由宏观级控制器设置的期望的参考命令;（3）利用PTV-1000仿真软件，对基于分层学习的城市和公路交通网络控制模式的有效性进行了验证。VISSIM交通模拟软件。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进行评估，被认为值得支持审查标准。