An Energy-efficient and Self-diagnostic Portable Edge-Computing Platform for Traffic Monitoring and Safety

Recent advances in technologies have shown great potential for widespread use of Artificial Intelligence (AI) techniques in real-time Intelligent Transportation Systems (ITS) applications. However, the massive amounts of data collected and generated from ITS sensors pose a major challenge in data processing and transmission. This requires a shift from centralized repositories and cloud computing to edge computing. This project proposes an integrated low-power edge-computing system to work with computation-intensive traffic sensors (e.g., video, high-resolution radar, and Lidar) and weather sensors. The system will be designed to be portable, have self-diagnostic capabilities through monitoring sensors and system operations, and send out alerts and data when necessary. The proposed system will include an edge server, which will be developed based on a System-on-Module (SoM) using the latest AI chip, and an innovative hybrid camera that integrates a regular video camera and a FLIR thermal image camera. The project will identify and implement in-situ information processing and extraction algorithms based on machine learning and deep learning techniques to classify vehicles and detect events such as vehicle crashes, the presence of stopped vehicles, pavement and environmental conditions, and wildlife. The prototype will be demonstrated at a California test site in collaboration with Caltrans.