Aerial Traffic
| dc.contributor.author | Viray, Reginald | en |
| dc.contributor.author | Saffy, Joshua | en |
| dc.contributor.author | Mollenhauer, Michael A. | en |
| dc.date.accessioned | 2024-08-30T19:04:31Z | en |
| dc.date.available | 2024-08-30T19:04:31Z | en |
| dc.date.issued | 2024-08-30 | en |
| dc.description.abstract | This report documents a significant advancement in work-zone safety through the strategic integration of aerial drone technology and machine vision software. It summarizes the project’s phases: Technical Assessment and Procurement, System Integration and Validation, and Deployment Assessment. The Technical Assessment and Procurement phase led to the selection of Smartek ITS’s DataFromSky product for its unique real-time processing capabilities of aerial drone video, making it superior to other commercial offerings. The System Integration and Validation phase ensured that the video streams, whether real-time or recorded, were processed effectively for varying roadway scenarios, including work zones and intersection monitoring. Accompanying development work included a user-defined data interface with the capability to trigger intruding vehicle alarms. The Deployment Assessment phase confirmed the system’s precision, with object detection up to 150 meters and sub-500 millisecond latency in relaying data for real-time alerts. Despite some GPS data discrepancies due to wind-induced drone movements, the system showed promise in controlled and real-world environments. Overall, the project acquired and validated the system’s functionality, with successful tests on live and recorded video feeds, software video processing, and real-time data transmission, culminating in the development of a robust intruding vehicle alarm mechanism. The system demonstrated great potential for deployment across various Virginia Tech Transportation Institute research initiatives, setting a precedent for future work in enhancing work zone safety. | en |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.uri | https://hdl.handle.net/10919/121039 | en |
| dc.language.iso | en | en |
| dc.publisher | National Surface Transportation Safety Center for Excellence | en |
| dc.relation.ispartofseries | NSTSCE; 24-UR-157 | en |
| dc.rights | Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International | en |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | en |
| dc.subject | work zone safety | en |
| dc.subject | computer vision | en |
| dc.subject | machine learning | en |
| dc.subject | drones | en |
| dc.subject | connected infrastructure | en |
| dc.title | Aerial Traffic | en |
| dc.type | Technical report | en |
| dc.type.dcmitype | Text | en |