In recent years, tremendous progress has been made in understanding the dynamics of vehicle traffic flow and traffic congestion by interpreting traffic as a multi-particle system. This helps to explain the onset and persistence of many undesired phenomena, e.g., traffic jams. It also reflects the apparent helplessness of drivers in traffic, who feel like passive particles that are pushed around by exterior forces; one of the crucial aspects is the inability to communicate and coordinate with other traffic participants. We present distributed methods for solving these fundamental problems, employing modern wireless, ad-hoc, multi-hop networks. The underlying idea is to use these capabilities as the basis for self-organizing methods for coordinating data collection and processing, recognizing traffic phenomena, and changing their structure by coordinated behavior. The overall objective is a multi-level approach that reaches from protocols for local wireless communication, data dissemination, pattern recognition, over hierarchical structuring and coordinated behavior, all the way to large-scale traffic regulation. In this article we describe three types of results: (i) self-organizing and distributed methods for maintaining and collecting data (using our concept of Hovering Data Clouds); (ii) adaptive data dissemination for traffic information systems; (iii) methods for self-recognition of traffic jams. We conclude by describing higher-level aspects of our work.
Generative AI-empowered Simulation for Autonomous Driving in Vehicular Mixed Reality Metaverses
Nanyang Technological University（南洋理工大学）；Peking University（北京大学）；Guangdong University of Technology（广东工业大学）；Singapore University of Technology and Design（新加坡科技设计大学）；Auburn University（奥本大学）；University of Houston（休斯顿大学）；Kyung Hee University（庆熙大学）
Vehicular Ad Hoc and Sensor Networks; Principles and Challenges
车辆交通的迅速增加和公路上的拥挤开始妨碍交通的安全和有效流动。因此，我们看到大多数国家的车祸和伤亡率逐年上升。因此，利用新技术，如无线传感器网络，需要作为一个解决方案，减少这些令人伤心和reconsiderable统计。这促使我们提出了一种新颖的和全面的系统，利用无线传感器网络的车辆网络。我们将采用无线传感器网络的车载网络称为车载自组织传感器网络（Vehicular Ad Hoc and Sensor Network，简称VASNET）。VASNET是一个由大量传感器节点组成的自组织Ad Hoc传感器网络，是一个专门针对高速公路交通的网络。在VASNET中，有两种传感器节点，一些是嵌入在车辆上的车辆节点-和其他部署在预定的距离以外的高速公路，被称为路边传感器节点（RSS）。车辆节点例如用于感测车辆的速度。我们可以有一些基站（BS），如警察交通局，消防队和救援队。基站可以是固定的或移动的。VASNET提供了车辆节点和固定节点之间的无线通信能力，以增加高速公路上车辆的安全性和舒适性。在本文中，我们解释VASNET的主要基础和挑战。
Leveraging AI and Intelligent Reflecting Surface for Energy-Efficient Communication in 6G IoT
Shanghai Jiao Tong University（上海交通大学）；Macquarie University（麦考瑞大学）；University of Technology Sydney（悉尼科技大学）；Fuzhou University（福州大学）；Lulea University of Technology（吕勒奥理工大学）
