CAREER: Distributionally Robust Learning, Control, and Benefits Analysis of Information Sharing for Connected and Autonomous Vehicles

无处不在的传感、通信和计算技术的快速发展促成了信息物理系统（CPS）的革命。基于学习的方法集成到物理系统的控制中，并在许多CPS领域展示了令人印象深刻的性能，连接和自动驾驶汽车（cav）系统就是车辆到一切通信技术发展的一个例子。然而，现有文献对沟通、学习和控制三者之间的三向关系认识不足。需要解决的主要挑战包括：(1)如何利用共享信息对动态系统状态和状态不确定性进行建模；(2)如何在模型不确定性下做出鲁棒的学习和控制决策；(3)如何将学习和控制相结合以保证网络化CPS的安全；(4)如何量化通信的效益。为了应对这些挑战，本CAREER提案旨在设计集成的通信、学习和控制规则，这些规则对混合系统模型的不确定性具有鲁棒性，以实现自动驾驶汽车的安全运行和系统效率。其关键的智力优势在于设计了具有严格安全保证的集成分布式鲁棒多智能体强化学习（DRMARL）和控制框架，考虑了共享信息预测混合系统状态的不确定性，为分析和量化通信效益提供了科学基础。基本理论和算法原理将通过模拟器、小规模试验台和全尺寸自动驾驶汽车现场演示进行验证，形成一个新的框架，用于自动驾驶汽车和网络化CPS的未来连接、学习和控制。技术贡献如下。（1). 在共享信息的情况下，我们将设计一种协作预测算法，以改进学习和控制所需的混合系统状态和模型不确定性表示。（2). 考虑到增强的预测能力，我们将设计一个具有严格安全保证的集成DRMARL和控制框架，以及计算易于处理的混合系统决策策略计算算法。这集成了学习和控制的优势，以提高系统的安全性和效率。（3). 我们将正式定义和量化沟通的价值，并提出一种新的学习沟通的方法，利用学习和控制来改善沟通行为。本项目还将把教育计划与研究目标相结合，开发“ssCAVs”作为教育工具的学习平台和“学习与控制”的跨学科新课程，面向公众和K-12学生和教师，直接让高中学者、本科生和研究生参与研究。本项目响应NSF CAREER 20-525招标。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 rapid evolution of ubiquitous sensing, communication, and computation technologies has contributed to the revolution of cyber-physical systems (CPS). Learning-based methodologies are integrated to the control of physical systems and demonstrating impressive performance in many CPS domains and connected and autonomous vehicles (CAVs) system is one such example with the development of vehicle-to-everything communication technologies. However, existing literature still lacks understanding of the tridirectional relationship among communication, learning, and control. The main challenges to be solved include (1) how to model dynamic system state and state uncertainties with shared information, (2) how to make robust learning and control decisions under model uncertainties, (3) how to integrate learning and control to guarantee the safety of networked CPS, and (4) how to quantify the benefits of communication.To address these challenges, this CAREER proposal aims to design integrated communication, learning, and control rules that are robust to hybrid system model uncertainties for safe operation and system efficiency of CAVs. The key intellectual merit is the design of integrated distributionally robust multi-agent reinforcement learning (DRMARL) and control framework with rigorous safety guarantees, considering hybrid system state uncertainties predicted with shared information, and the development of scientific foundation for analyzing and quantifying the benefits of communication. The fundamental theory and algorithm principles will be validated using simulators, small-scale testbeds, and full-scale CAVs field demonstrations, to form a new framework for future connectivity, learning, and control of CAVs and networked CPS. The technical contributions are as follows. (1). With shared information, we will design a cooperative prediction algorithm to improve hybrid system state and model uncertainty representations needed by learning and control. (2). Given enhanced prediction, we will design an integrated DRMARL and control framework with rigorous safety guarantee, and a computationally tractable algorithm to calculate the hybrid system decision-making policy. This integrates the strengths of both learning and control to improve system safety and efficiency. (3). We will define formally and quantify the value of communication given and propose a novel learn to communicate approach, to utilize learning and control to improve the communication actions. This project will also integrate an educational plan with the research goals by developing a learning platform of ``ssCAVs'' as an education tool and new interdisciplinary courses on “learning and control”, undertaking outreach to the general public and K-12 students and teachers, and directly involving high-school scholars, undergraduate and graduate students in research. This project is in response to the NSF CAREER 20-525 solicitation.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.