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

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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.