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

• 批准号: 2130734
• 负责人: Ali Mesbah
• 金额: $ 27.67万
• 依托单位: University of California-Berkeley
• 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=2130734&HistoricalAwards=false
• 关键词: Collaborative; Research; Learning; Based; Scalable

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.

随着联网和自动化汽车技术越来越多地被公众接受并得到监管机构的批准，这种技术的广泛采用可能会在几年内发生。在此之前，至关重要的是制定交通管理策略，考虑交通网络中与异质性相关的不确定性，并了解这些策略在多大程度上改善了交通网络的性能。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，以增强不同类型的交通网络，解决人工驾驶和自动化车辆、机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。具体地说，这些控制方法将有助于运输机构了解如何利用基于基础设施的战略来提高混合交通环境中的能源效率和机动性。为自动驾驶车辆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，本研究中开发的模型和技术有望在广泛的应用中产生影响，在这些应用中，系统的行为可以被建模为不确定的异质系统，例如执行搜索和救援任务的空中和地面移动机器人。该教育计划旨在影响研究生和本科生、K-12学生和少数族裔学生准备和参与多样化的STEM工作。这项合作研究旨在开发一个框架，用于对由自动驾驶和人工驾驶车辆组成的异质交通网络进行易处理的建模和优化控制。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)在上层(宏观)发展基于学习和情景的分布式模型预测控制方法，其中将使用泛函变分贝叶斯神经网络对与交通网络中的异质性相关的状态和输入相关的不确定性进行建模，并将使用分布式优化算法来提高所提出的控制方法的计算效率；(2)在较低(微观)级别开发基于分布式谨慎模型预测控制的方法，以确保单个车辆的安全，同时跟踪宏观层控制器设定的期望参考命令；(3)使用PTV-VISSIM交通模拟软件测试基于分层学习的控制范例在城市和公路交通网络中的有效性。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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

Traffic Congestion Control Using Distributed Extremum Seeking and Filtered Feedback Linearization Control Approaches

• DOI: 10.1109/lcsys.2022.3229267
• 发表时间: 2023
• 期刊: IEEE Control Systems Letters
• 影响因子: 3
• 作者: Pouria Karimi Shahri;B. Homchaudhuri;S. Pulugurtha;A. Mesbah;A. Ghasemi