Award Data

Real-time monitoring of traffic conditions is crucial for improving traffic safety and efficiency, and there is an urgent need for the development of in situ traffic sensing & computing technologies leveraging the recent advancements in edge computing. This project will develop an edge computing and sensor fusion system for comprehensive traffic and road conditions data collection and efficient tr ...

While AI-based techniques have been broadly embraced by transportation researchers and professionals due to their huge potential to significantly improve the efficiency and accuracy of analyzing transportation data, they typically require a large dataset to train the models effectively and experience difficulties in supporting real-time transportation operations and management. This project will u ...

According to a survey completed by NCAT in 2019, as part of the NCHRP 20‐07 project, 36 DOTs (out of 43 responded) consider fatigue cracking as the most common failure mode that the agency wants to address. However, in the same survey, 34 of the DOTs also indicated that fatigue cracking test is not required in their mix design specifications. This is primarily because a simple, practical, and ro ...

While AI-based techniques have been broadly embraced by transportation researchers and professionals due to their huge potential to significantly improve the efficiency and accuracy of analyzing transportation data, they typically require a large dataset to train the models effectively and experience difficulties in supporting real-time transportation operations and management. This project will u ...

In CMV operations, impaired alertness from driver fatigue contributes to a significant number of collisions and safety critical events. This project (through Phase II) will achieve a system to deliver an objective quantifiable measure of driver alertness based on the Psychomotor Vigilance Test (PVT). This system will alert drivers when assessments indicate alertness deficits and support the timely ...

CoVar’s DOCTRINAIRE is a new approach to computer aided object annotation that is modeled after the way expert end-users leverage generic, robust background information (e.g., what wheels look like) and known doctrine (the size and shape of components on a pickup truck) to perform reliable, explainable object detection and annotation. Our approach solves the robustness problem by training a reli ...

The primary goal of this proposed project is to develop a general-purpose technique for the problem of algorithm parameter selection (APS) that achieves the best performance of the algorithm and demonstrates its effectiveness within graph analysis, for the problem of community detection, or clustering.  Cell Matrix Corporation (CMC) will develop a means to greatly simplify the tweaking typically ...

LUCID (Land Use Classification Intelligence Discovery) is a novel artificial intelligence (AI) system that achieves on-demand classification of fine-grained Land Use and Land Cover (LULC) categories to create detailed LULC-annotated geospatial grid maps by orchestrating seamless fusion of satellite imagery, remote sensing, and non-imagery geospatial data layers. LUCID fuses multiple data sources/m ...

In CMV operations, impaired alertness from driver fatigue contributes to a significant number of collisions and safety critical events. This project (through Phase II) will achieve a system to deliver an objective quantifiable measure of driver alertness based on the Psychomotor Vigilance Test (PVT). This system will alert drivers when assessments indicate alertness deficits and support the timely ...

According to a survey completed by NCAT in 2019, as part of the NCHRP 20‐07 project, 36 DOTs (out of 43 responded) consider fatigue cracking as the most common failure mode that the agency wants to address. However, in the same survey, 34 of the DOTs also indicated that fatigue cracking test is not required in their mix design specifications. This is primarily because a simple, practical, and ro ...