  Vehicle Trajectory datasets that provide multiple point-of-views (POVs) can
be valuable for various traffic safety and management applications. Despite the
abundance of trajectory datasets, few offer a comprehensive and diverse range
of driving scenes, capturing multiple viewpoints of various highway layouts,
merging lanes, and configurations. This limits their ability to capture the
nuanced 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}})}, a
vehicle trajectory, detection, and tracking dataset. \emph{CHD} is a collection
of 1.6 million frames captured in highway-based videos from eye-level and
high-angle POVs at eight locations across Carolinas with 338,000 vehicle
trajectories. The locations, timing of recordings, and camera angles were
carefully 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 trajectory
prediction architecture that uses attention-based graph isomorphism and
convolutional neural networks. The results demonstrate that \emph{PishguVe}
outperforms existing algorithms to become the new state-of-the-art (SotA) in
bird's-eye, eye-level, and high-angle POV trajectory datasets. Specifically, it
achieves a 12.50\% and 10.20\% improvement in ADE and FDE, respectively, over
the 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\% and
27.38\%, respectively, and improves ADE and FDE on high-angle data by 8.3\% and
6.9\%, respectively.
