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

随着互联和自动驾驶汽车技术越来越被公众所接受，并得到监管部门的批准，这种技术的广泛采用可能需要数年时间。在此之前，有必要制定交通管理策略，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，以增强异构交通网络，解决人为驾驶和自动驾驶车辆，移动性和能源效率问题。市政当局、交通运输机构、汽车工业和设备制造商将对该项目的成果感兴趣。具体来说，这些控制方法将有助于交通运输机构了解如何利用基于基础设施的策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导航的需求。此外，本研究中开发的模型和技术有望对系统行为可以建模为不确定异构系统的广泛应用产生影响，例如在搜索和救援任务中操作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学生和少数民族学生，以准备和参与多样化的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可处理建模和最优控制。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）层面开发分布式学习和基于场景的模型预测控制方法，其中将使用功能变分贝叶斯神经网络对交通网络异质性相关的状态和输入依赖的不确定性进行建模，并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)针对低层次（微观）异构多智能体系统，开发基于分布式谨慎模型预测控制的方法，在跟踪宏观层次控制器期望的参考命令集的同时，保证单个车辆的安全；(3)利用PTV-VISSIM交通仿真软件对基于分层学习的城市和公路交通网络控制范式进行有效性检验。该奖项反映了美国国家科学基金会的法定使命，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。

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.