An Airborne Lidar Scanning and Deep Learning System for Real-time Event Extraction and Control Policies in Urban Transportation Networks
| 项目名称： | An Airborne Lidar Scanning and Deep Learning System for Real-time Event Extraction and Control Policies in Urban Transportation Networks |
| 摘要： | The project team is currently investigating the capability to provide transportation and mobility solutions driven by real-time data generated from UAS using lidar and event identification through deep learning. Specific project tasks include: 1) developing optimal UAS-based lidar acquisition methodologies (payloads, sensor settings, and processing strategies) for transportation network scanning; 2) designing, implementing, and testing a deep learning algorithm that can extract features from the UAS lidar data, and 3) developing guidelines for state DOTs and other transportation agencies on the technical and operational requirements for UAS-based lidar data integration. The OSU project team recently integrated a Velodyne Puck lidar system and OxTS xNAV direct-georeferencing system on a DJI S1000 remote aircraft and have conducted test flights under an FAA-issued Certificate of Authorization (COA). Next steps will include working with ODOT to identify project sites to scan with the UAS-based lidar and transmitting the data to the UI project partners for implementing and testing the deep learning algorithms. |
| 状态： | Completed |
| 资金： | 180000 |
| 资助组织： | Office of the Assistant Secretary for Research and Technology |
| 管理组织： | Oregon State University, Corvallis |
| 项目负责人： | Parrish, Christopher |
| 执行机构： | University of Idaho, Moscow |
| 主要研究人员： | Hurwitz, David |
| 开始时间： | 20170816 |
| 预计完成日期： | 20190815 |
| 实际结束时间： | 20191231 |
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