Learning-Aided Distributed Estimation and Control for Networked Vehicular Systems

现代联网车辆系统正在利用互联技术的进步推动全球主要市场的创新。例如，智能交通是世界上发展最快的行业之一，它将车辆和基础设施连接起来，从而大幅提高燃油效率（高达22%）和乘客安全（冬季条件下事故减少25%）。然而，在这些复杂的、相互连接的系统中，用于数据融合、估计和控制的传统集中式体系结构是非常低效的（计算上）。集中式系统具有有限的灵活性和模块化，使网络容易受到故障和干扰的影响，这些故障和干扰可能会从单点故障中危及整个系统。新的网络控制系统建立在通过通信网络交换信息的子控制数据系统之上，并利用分布式、机器学习增强的算法，为这些挑战提供了一个有希望的解决方案。为日益流行的网络物理系统、自动驾驶系统（ADS）和协作车辆提供快速和可重构机制的潜力，凸显了开发更可靠的分布式系统设计的迫切需求。由于网络系统日益复杂，当前基于模型的分布式控制方法已经达到了性能极限，从而影响了模型的决策预测能力以及系统对意外事件和通信干扰的恢复能力。因此，拟议的研究计划将利用描述每个子系统主要特性的实验数据，通过这些网络节点之间的宽带通信（具有高数据速率）提供，为联网车辆系统推进一个新的学习辅助分布式估计和控制平台。总体而言，长期目标是为联网车辆系统开发一种新的控制和诊断范例，通过控制和学习算法的协同设计，提高可靠性和性能。在未来五年内，该团队将通过结合基于模型和基于学习的结构，利用5G NR等新无线接入技术提供的更低延迟和更高数据速率，解决使分布式系统更具计算效率和可靠性的核心挑战。将追求两个综合目标：1)网络系统中的学习辅助分布式估计；2)分布式学习控制算法的发展。其结果将是设计一个可扩展和弹性的分布式框架，用于连接ADS和智能交通，而不需要子系统节点知道确切的全球系统模型，从而为分布式系统的学习和控制能力提供潜在的突破。该团队还将为加拿大的智能交通行业培训下一代创新者。

Modern networked vehicular systems are leveraging advances in connectivity to drive innovation in major markets across the globe. Intelligent transportation, for example, which connects vehicles and infrastructure to enable dramatic improvements in fuel efficiency (up to 22%) and passenger safety (25% fewer accidents in winter conditions), is one of the world's fastest growing industries. However, traditional centralized architectures for data fusion, estimation, and control in these complex, interconnected systems are prohibitively inefficient (computationally). Centralized systems have limited flexibility and modularity, rendering the network susceptible to faults and disturbances that could imperil the entire system from just a single point of failure. New networked control systems built upon sub-control data systems that exchange information through a communication network and that leverage distributed, machine learning-enhanced algorithms present a promising solution to these challenges. The potential to enable fast and reconfigurable mechanisms for increasingly prevalent cyber-physical systems, Automated Driving Systems (ADS), and cooperative vehicles, underscores the critical need to develop more reliable distributed-system designs. Current model-based distributed control approaches are reaching their performance limits due to the growing complexity of such networked systems, therefore impacting the model's predictive capacity for decision-making and the resilience of the system to unexpected events and communication disturbances. Therefore, the proposed research program will advance a new learning-aided distributed estimation and control platform for networked vehicular systems using experimental data that describes the main properties of each subsystem, provided through broadband communication (with high data rates) across these networked nodes. The overarching, long-term goal is to develop a new control and diagnosis paradigm for networked vehicular systems, enabling increased reliability and performance through co-design of control and learning algorithms. Through the next five years, the team will address core challenges to rendering distributed systems more computationally efficient and reliable by combining model- and learning-based structures, taking advantage of the lower latency and higher data rates provided by new radio access technologies such as 5G NR. Two integrated objectives will be pursued: 1) Learning-aided distributed estimation in networked systems; and 2) Development of distributed learning control algorithms. The result will be design of a scalable and resilient distributed framework for connected ADS and intelligent transportation without requiring the exact global system model to be known to the subsystem nodes-offering potential breakthroughs in the distributed system's learning and control capacity. The team will also train the next generation of innovators for Canada's intelligent transportation industry.