Deep-Learning Based Trajectory Forecast for Safety of Intersections with Multimodal Traffic
| 项目名称： | Deep-Learning Based Trajectory Forecast for Safety of Intersections with Multimodal Traffic |
| 摘要： | With the convergence of computation, communication, sensing, and visualization into ever smaller and cheaper devices, the United States is entering in a new era of more efficient, safer and more affordable transportation. The recent emergence of novel augmented reality technologies in particular offer a formidable opportunity to improve the safety of traffic for all road users. In this proposal, the objective is to investigate the potential of augmented reality, in conjunction with deep-learning based image processing, for traffic safety at intersections, considering all possible modes of transportation. A major problem of a safety system is to offer reliable, timely warnings, with a low false detection rate. This somehow requires the prediction of the future trajectories of the users, which is the primary focus of this proposal. In this project, the goal is to investigate how deep learning can be used to detect motion cues, and estimate over a short time horizon the future path of road users (depending on their transportation modes) using real-time video data. Once estimated, these paths will be used as part of an augmented-reality based system, where information about potential conflicts is displayed in the field of view of all road users, through smart glasses. |
| 状态： | Completed |
| 资金： | 90821 |
| 资助组织： | Office of the Assistant Secretary for Research and Technology |
| 管理组织： | University of North Carolina, Charlotte |
| 项目负责人： | Fan, Wei (David) |
| 执行机构： | University of Texas at Austin |
| 主要研究人员： | Claudel, Christian |
| 开始时间： | 20171001 |
| 预计完成日期： | 20190930 |
| 实际结束时间： | 20190930 |
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