Cloud-based Collaborative Road Condition Monitoring using In-Vehicle Smartphone Data and Deep Learning

Description: Ensuring the safety of transportation systems, especially multimodal connected and autonomous transportation systems, requires monitoring the conditions of roads. Traditional monitoring and inspection of road conditions require surveyors to walk or drive along the roads to search for defects manually. Such processes require a lot of human and equipment efforts, which however can still hardly provide timely needed information of road conditions. Existing automated road condition monitoring approaches usually require special vehicles equipped with specific sensors and corresponding processing and computing devices, which increases the cost of the approaches. In addition, these existing approaches only use one single vehicle to perform the detection on its own and the vehicle usually still needs to be driven by a surveyor, which still requires a large number of efforts to monitor the roads in terms of labor and equipment costs. Therefore, a more cost-effective and efficient road condition monitoring approach is needed. Intellectual Merit: The research goal is to develop a cost-effective approach to monitor the road conditions by cloud-based collaborative monitoring using in-vehicle smartphones which could be from any general public vehicle users. Broader Impacts: This project aims to reduce the cost of road condition monitoring by providing a very cost-effective way with a minimum investment of equipment and labor, significantly improve the safety of transportation systems, especially the multimodal connected and automated transportation systems, by providing timely needed road condition monitoring, and create a smartphone-based road condition dataset to benefit the research society.