Learning-Aided Distributed Estimation and Control for Networked Vehicular Systems

现代联网车载系统正在利用连接方面的进步来推动全球主要市场的创新。例如，智能交通是世界上增长最快的行业之一，它将车辆和基础设施连接起来，使燃油效率(高达22%)和乘客安全(冬季条件下的事故减少25%)得到显著改善。然而，在这些复杂的、相互关联的系统中，用于数据融合、估计和控制的传统集中式体系结构(在计算上)效率低得令人望而却步。集中式系统的灵活性和模块化有限，使得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个故障点危及整个系统。建立在子控制数据系统上的新型网络控制系统通过通信网络交换信息，并利用分布式、机器学习增强的算法，为这些挑战提供了一种有前途的解决方案。为日益普遍的网络物理系统、自动驾驶系统(ADS)和合作车辆启用快速和可重新配置的机制的潜力，突显了开发更可靠的分布式系统设计的迫切需要。当前基于模型的分布式控制方法由于这类网络系统的日益复杂而达到其性能极限，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，拟议的研究计划将提出一个新的学习辅助的分布式估计和控制平台，用于联网的车辆系统，使用描述每个子系统的主要属性的实验数据，通过这些联网节点的宽带通信(高数据速率)提供。最重要的长期目标是为联网的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联合设计来提高可靠性和性能。在接下来的五年里，该团队将通过结合基于模型和基于学习的结构，利用5G NR等新的无线接入技术提供的更低延迟和更高的数据速率，解决使分布式系统在计算上更高效和更可靠的核心挑战。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。其结果将是为互联广告和智能交通设计一个可扩展和有弹性的分布式框架，而不需要子系统节点知道确切的全球系统模型-提供分布式系统学习和控制能力的潜在突破。该团队还将为加拿大智能交通行业培养下一代创新者。

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.