Autonomous Drive-by Monitoring Technologies for Developing Smart Transportation Infrastructure Systems

我们现代的宏伟事业之一是建设智能，可持续和有弹性的城市，这些城市可以有效地管理和运营，同时为居民提供高水平的舒适度。实现这一目标的道路本身就很复杂，而且是多方面的，这需要我们对基础设施的管理进行根本性的转变。开发一个具有更高容量，效率和弹性的智能交通基础设施系统，可以使用信息技术有效地运营和维护，是这项工作的关键一步。 智能基础设施的一个重要组成部分是持续感知和监控。为了促进智能和可持续交通基础设施的发展，本研究提出了一种新的范式，用于使用众包数据对全球交通基础设施系统的人口进行连续和自动化监测。由于传感技术和物联网的快速发展，我们的交通基础设施上使用的车辆以及车辆乘客的智能手机通常配备了各种传感器，如加速度计，摄像头和GPS。通过拟议的研究，将开发利用众包车辆内现有传感器自动监测交通基础设施人口的方法。 在这个框架中，每个车辆作为一个移动的传感节点进行数据收集。一旦收集到数据，将使用机载处理器对其进行预处理，并将提取的特征传输到远程数据库进行进一步分析和决策。嵌入车辆智能手机中的加速度计将用于捕获桥梁和车辆的耦合振动。还将开发先进的信号处理技术，利用大量车辆中智能手机的振动数据，以自动化的方式识别桥梁群体损坏的存在、位置和严重程度。此外，拟议的研究将开发涉及深度学习的有效图像处理技术，用于使用安装在车辆中的摄像头实时高精度地自动检测道路或桥面表面的缺陷（例如，备份和仪表盘摄像机）。 据研究人员所知，该框架是世界上第一个此类框架，将导致颠覆性技术和新的研究领域。所提出的系统有可能连续监测人口的交通基础设施系统自动在真实的时间。拟议研究的预期成果将有助于加拿大基础设施所有者提高现有基础设施的安全性和可持续性，并为未来的智能城市创建可持续的智能基础设施。未来，该框架可以扩展到使用现有传感器的智能和互联自动驾驶汽车。

One of the grand undertakings of our modern age is to build smart, sustainable, and resilient cities that can be managed and operated efficiently while providing residents with a high level of comfort. The path to achieving this is inherently complex and multi-dimensional, and it requires a fundamental shift in the management of our infrastructure. Developing a smart transportation infrastructure system with higher capacity, efficiency, and resilience, which can be operated and maintained effectively using information technologies, is a key step in this undertaking. One important component of smart infrastructure is continuous sensing and monitoring. To contribute to the development of smart and sustainable transportation infrastructure, this research proposes a novel paradigm for continuous and automated monitoring of populations of transportation infrastructure systems globally using data from crowdsourcing. Owing to rapid advances in sensing technologies and IoT, the vehicles used on our transportation infrastructure, as well as the smartphones of the passengers in vehicles, are usually equipped with various sensors such as accelerometers, cameras, and GPS. Through the proposed research, methods will be developed for automated monitoring of populations of transportation infrastructure capitalizing on the existing sensors within crowdsourced vehicles. In this framework, each vehicle serves as a mobile sensing node for data collection. Once the data has been collected, it will be pre-processed using on-board processors, and extracted features will be transmitted to a remote database for further analysis and decision making. Accelerometers embedded in the smartphones in the vehicles will be used to capture the coupled vibration of bridges and vehicles. Advanced signal processing techniques will also be developed for identifying the existence, location, and severity of the damage on populations of bridges in an automated fashion using the vibration data from smartphones in a large number of vehicles. Additionally, the proposed research will develop effective image processing techniques involving deep learning for automated detection of defects on road or bridge deck surfaces with high accuracy in real time using the cameras installed in vehicles (e.g., backup and dash cameras). To the best of the researcher's knowledge, this framework is the first of its kind in the world, and will lead to a disruptive technology and a new area of research. The proposed system has the potential to continuously monitor populations of transportation infrastructure systems automatically in real time. Expected outcomes of the proposed research will help Canada's infrastructure owners to increase the safety and sustainability of existing infrastructure, and to create sustainable smart infrastructure for the smart cities of the future. In the future, the proposed framework can be extended to use with smart and connected autonomous vehicles employing existing sensors.