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Vehicle Trajectory datasets that provide multiple point-of-views (POVs) canbe valuable for various traffic safety and management applications. Despite theabundance of trajectory datasets, few offer a comprehensive and diverse rangeof driving scenes, capturing multiple viewpoints of various highway layouts,merging lanes, and configurations. This limits their ability to capture thenuanced interactions between drivers, vehicles, and the roadway infrastructure.We introduce the \emph{Carolinas Highway Dataset (CHD\footnote{\emph{CHD}available at: \url{https://github.com/TeCSAR-UNCC/Carolinas\_Dataset}})}, avehicle trajectory, detection, and tracking dataset. \emph{CHD} is a collectionof 1.6 million frames captured in highway-based videos from eye-level andhigh-angle POVs at eight locations across Carolinas with 338,000 vehicletrajectories. The locations, timing of recordings, and camera angles werecarefully selected to capture various road geometries, traffic patterns,lighting conditions, and driving behaviors.  We also present \emph{PishguVe}\footnote{\emph{PishguVe} code available at:\url{https://github.com/TeCSAR-UNCC/PishguVe}}, a novel vehicle trajectoryprediction architecture that uses attention-based graph isomorphism andconvolutional neural networks. The results demonstrate that \emph{PishguVe}outperforms existing algorithms to become the new state-of-the-art (SotA) inbird's-eye, eye-level, and high-angle POV trajectory datasets. Specifically, itachieves a 12.50\% and 10.20\% improvement in ADE and FDE, respectively, overthe current SotA on NGSIM dataset. Compared to best-performing models on CHD,\emph{PishguVe} achieves lower ADE and FDE on eye-level data by 14.58\% and27.38\%, respectively, and improves ADE and FDE on high-angle data by 8.3\% and6.9\%, respectively.

A POV-based Highway Vehicle Trajectory Dataset and Prediction Architecture