SCC: Video Based Machine Learning for Smart Traffic Analysis and Management

点击翻译按钮获取中文摘要

The goal of this project is to further the ability of cities and communities to deploy technology that saves lives through safer transportation systems. The approach is to create open source analytics solutions to enable novel transportation applications that utilize data from low-cost video sensors. Video data are processed using edge computing (inexpensive computing hardware that performs analysis without storing significant amounts of data) in order to reduce the amount of data stored. Social dimensions of the research project emerge from the deep research partnership between the City and the University, with the goal to provide replicable and near-term social impacts. The project aligns with the Vision Zero concept to reduce traffic fatalities, with programs that are based on education, enforcement and design. By understanding the risk profile of an intersection through automated detection of near miss events, communities will be able to proactively design and alter streets and intersections to be safer. The goal of designing a smart city, when addressing the technical challenges at the intersection, street and system levels, has several research components. (i) Development of new algorithms for multi-target tracking: The problems of occlusion, temporal assignment of features to objects and target motion will be jointly formulated. (ii) Integrated optimization and simulation for signal control: We formulate the problem of estimating signal control parameters (offsets, phasing etc.) in a network as one of global optimization. (iii) Real-time reinforcement learning is a natural choice when online machine learning meets real world feedback from the City. Our ability to obtain and analyze continuous-time data at the network level will provide insights on how conflict points and patterns can change through the network. This is expected to impact decisions in traffic management, smart city planning and safety.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.