Browsing by Author "Viray, Reginald"

Now showing 1 - 7 of 7
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    Aerial Traffic
    Viray, Reginald; Saffy, Joshua; Mollenhauer, Michael A. (National Surface Transportation Safety Center for Excellence, 2024-08-30)
    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.
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    Connected Motorcycle System Performance
    Viray, Reginald; Noble, Alexandria M.; Doerzaph, Zachary R.; McLaughlin, Shane B. (Connected Vehicle/Infrastructure University Transportation Center (CVI-UTC), 2016-01-15)
    This project characterized the performance of Connected Vehicle Systems (CVS) on motorcycles based on two key components: global positioning and wireless communication systems. Considering that Global Positioning System (GPS) and 5.9 GHz Dedicated Short-Range Communications (DSRC) may be affected by motorcycle rider occlusion, antenna mounting configurations were investigated. In order to assess the performance of these systems, the Virginia Tech Transportation Institute’s (VTTI) Data Acquisition System (DAS) was utilized to record key GPS and DSRC variables from the vehicle’s CVS Vehicle Awareness Device (VAD). In this project, a total of four vehicles were used where one motorcycle had a forward mounted antenna, another motorcycle had a rear mounted antenna, and two automobiles had centermounted antennas. These instrumented vehicles were then subject to several static and dynamic test scenarios on closed test track and public roadways to characterize performance against each other. Further, these test scenarios took into account motorcycle rider occlusion, relative ranges, and diverse topographical roadway environments. From the results, both rider occlusion and approach ranges were shown to have an impact on communications performance. In situations where the antenna on the motorcycle had direct lineof-sight with another vehicle’s antenna, a noticeable increase in performance can be seen in comparison to situations where the line of sight is occluded. Further, the forward-mounted antenna configuration provided a wider span of communication ranges in open-sky. In comparison, the rear-mounted antenna configuration experienced a narrower communication range. In terms of position performance, environments where objects occluded the sky, such as deep urban and mountain regions, relatively degraded performance when compared to open sky environments were observed.
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    Development of an Infrastructure Based Data Acquisition System to Naturalistically Collect the Roadway Environment
    Sarkar, Abhijit; Papakis, Ioannis; Herbers, Eileen; Viray, Reginald (2023-12)
    Automatic traffic monitoring is becoming an important investment for transportation specialists, especially as the overall volume of traffic continues to increase, as do crashes at intersections. Infrastructure cameras can be a good source of information for automatic monitoring of traffic situations at intersections. However, efficient computer vision methods that can process the video data effectively are required for this endeavor. Intersection cameras often record video data that are of low quality and low frame rate, making them challenging to use. In this project, we have demonstrated how traffic cameras can be used to automatically track roadway agents, find their kinematic behavior, and devise a safety measurement strategy, leveraging recent advancements in computer vision and deep learning. In this process, we have specifically focused on the Virginia Beach area and used publicly available traffic data to demonstrate our results. We developed a full computer vision pipeline that trains a custom object detector specifically using traffic data. We also used an optical flow method and a graph neural network to improve the accuracy of object tracking. The tracked objects from the image frames were further used as a point source and mapped to their GPS locations. Finally, the speed of each object was calculated to understand the traffic dynamics and determine possible crash predictors. This information can be used to quickly alert traffic control operators to a specific intersection that likely needs their attention so that crashes can be mitigated.
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    Radar-Based Over-the-Air Message Generator for Accelerating Connected Vehicle Deployment
    Viray, Reginald; Gorman, Thomas A.; Doerzaph, Zachary R. (Connected Vehicle/Infrastructure University Transportation Center (CVI-UTC), 2016-10-15)
    The market penetration levels needed to realize the full safety, economic, and environmental benefits of connected vehicle (CV) systems will not be met for some time. During the transition, it would be beneficial if data on non-CVs could be measured and included within the real-time CV data stream. Conceptually, a connected vehicle with advanced sensors, such as radar, could measure the dynamics of adjacent vehicles and, in addition to broadcasting its own Basic Safety Message (BSM), broadcast a pseudo BSM representing the non-connected vehicles. This project investigated the use of radar sensors to compute the position, speed, and heading of a non-connected vehicle (non-CV) for packaging into a pseudo BSM. An algorithm was developed to estimate the speed, position, and heading of a nearby non-CV via speed, Global Positioning System (GPS) coordinates, and radar data from the CV. Field tests were conducted with two vehicles on the Virginia Smart Road and on public roads in the New River Valley of Virginia. The field tests were designed to cover a variety of vehicle formations, traffic densities, velocities, and roadway environments. The final results showed that 67.9% of the position estimates were within 3 m of the measured position along the x-axis (longitudinal) and within 1.5 m of the measured position along the y-axis (lateral). Heading and speed estimates were generally excellent. Although the estimated position accuracy was lower than desired, the data that were collected and analyzed were sufficient to suggest ways to improve the system, such as fusing the radar data with camera-based vision data or using a more accurate GPS.
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    Rocky Mountain Naturalistic Driving Study
    Dunn, Naomi; Viray, Reginald (National Surface Transportation Safety Center for Excellence, 2024-06-17)
    The Rocky Mountain Naturalistic Driving Study (RMNDS) was, at the time of data collection, the first attempt to use the NDS methodology to conduct research on the effects of cannabis on driving performance. The resulting dataset comprises over 14,000 trips made by 23 participants who self-reported medium to heavy cannabis use and who also reported they had a history of driving under the influence of cannabis. A unique aspect of the study was the collection of quantitative and qualitative drug use data. Qualitative drug use data was collected via an online journal, while quantitative drug use data was collected using a Quantisal oral fluid collection device prior to one driving trip each week. Samples were sent to a toxicology laboratory for analysis, which produced quantifiable test results for the National Institute on Drug Abuse 5 drug panel, including delta-9-THC. Out of the 14,000+ trips, there were a total of 178 verified drug test results along with 1,549 drug use journal entries. While the study proved successful for collecting naturalistic driving data and both objective and subjective drug use data, the difficulty comes in linking the drug use data to the driving data in order to identify periods of driving that may be impacted by the consumption of cannabis and/or other drugs. Further analysis of the RMNDS data, including identification of trips linked to drug use, would provide invaluable information about the impact of cannabis and/or other drugs on driving performance.
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    Signal Awareness Applications
    Mollenhauer, Michael A.; Viray, Reginald; Doerzaph, Zachary R.; White, Elizabeth; Song, Miao (SAFE-D: Safety Through Disruption National University Transportation Center, 2022-09)
    Intersection collisions account for 40% of all crashes on U.S. roadways. It is estimated that 165,000 accidents, which result in approximately 800 fatalities annually, are due to vehicles that pass through intersections during red signal phases. Although infrastructure-based red-light violation countermeasures have been deployed, intersections remain a top location for vehicle crashes. The Virginia Department of Transportation and its research arm, the Virginia Transportation Research Council, partnered with the Virginia Tech Transportation Institute to create the Virginia Connected Corridors (VCC), a connected vehicle test bed located in Fairfax and Blacksburg, Virginia, that enables the development and assessment of early-stage connected and automated vehicle applications. Recently, new systems have been deployed that transmit position correction messages to support lane-level accuracy, enabling development of signal awareness applications such as red-light violation warning. This project enhances the current capabilities of VCC platforms by developing new signal awareness safety and mobility features. Additionally, this project investigated the technical and human factors constraints associated with user interfaces for notifying and alerting drivers to pertinent intersection-related information to curb unsafe driving behaviors at signalized intersections.
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    Virginia Connected Vehicle Test Bed System Performance (V2I System Performance)
    Viray, Reginald; Sarkar, Abhijit; Doerzaph, Zachary R. (Connected Vehicle/Infrastructure University Transportation Center (CVI-UTC), 2016-05-01)
    This project identified vehicle-to-infrastructure (V2I) communication system limitations on the Northern Virginia Connected Vehicle Test Bed. Real-world historical data were analyzed to determine wireless Dedicated Short Range Communication (DSRC) coverage gaps and overlaps. In addition, a simulated scalability test was run to determine the effects of network congestion on the system. The results from the real-world historical data showed that significant loss of signal occurred due to obstructions commonly found in complex highway systems, including overpasses and underpasses, elevated concrete roadways, and foliage. Consequently, care must be taken to minimize loss of signal when selecting an installation site for roadside equipment (RSEs). The deployment of multiple RSEs or repeaters may be necessary to maximize coverage in localized dead zones. The results from the scalability test showed that the current network architecture is not able to handle a large deployment of connected vehicles (CV). If a large scale of CV were to be deployed, an assessment of the current network design needs to be investigated to account for the number of vehicles and subsequent flow of data expected in the operational area.