Real Time Risk Prediction at Signalized Intersection Using Graph Neural Network
| 项目名称： | Real Time Risk Prediction at Signalized Intersection Using Graph Neural Network |
| 摘要： | Intersection related traffic crash and fatalities are one of the major concerns for road safety. In this project the team aims to understand the major cause of conflicts at an intersection by studying the intricate interplay between all the roadway agents. The project team proposes to use the current traffic camera systems to automatically process traffic video data. As manual annotation of video datasets is a very labor-intensive and costly process, A system that can process these traffic datasets automatically would strongly enhance the effectiveness of the analysis and enable new research questions to be addressed. Therefore, the project team proposes to use computer vision algorithm to process the videos. Also, the team proposes to use advanced machine learning methods including graph neural network (GNN) to model the interaction of all the roadway agents at any given instance, and their role in road safety, both individually and as a composite system. As a result, the proposed model aims to develop a near real time risk score for a traffic scene. |
| 状态： | Active |
| 资金： | 320000 |
| 资助组织： | Office of the Assistant Secretary for Research and Technology |
| 管理组织： | Safety through Disruption University Transportation Center (Safe-D) |
| 项目负责人： | Glenn, Eric Zachary |
| 执行机构： | Virginia Tech Transportation Institute |
| 主要研究人员： | Sarkar, Abhijit |
| 开始时间： | 20220501 |
| 预计完成日期： | 20230630 |
| 主题领域： | Data and Information Technology；Highways；Operations and Traffic Management；Safety and Human Factors |
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