  Artificial intelligence solutions for Autonomous Vehicles (AVs) have been
developed using publicly available datasets such as Argoverse, ApolloScape,
Level5, and NuScenes. One major limitation of these datasets is the absence of
infrastructure and/or pooled vehicle information like lane line type, vehicle
speed, traffic signs, and intersections. Such information is necessary and not
complementary to eliminating high-risk edge cases. The rapid advancements in
Vehicle-to-Infrastructure and Vehicle-to-Vehicle technologies show promise that
infrastructure and pooled vehicle information will soon be accessible in near
real-time. Taking a leap in the future, we introduce the first comprehensive
synthetic dataset with intelligent infrastructure and pooled vehicle
information for advancing the next generation of AVs, named VTrackIt. We also
introduce the first deep learning model (InfraGAN) for trajectory predictions
that considers such information. Our experiments with InfraGAN show that the
comprehensive information offered by VTrackIt reduces the number of high-risk
edge cases. The VTrackIt dataset is available upon request under the Creative
Commons CC BY-NC-SA 4.0 license at http://vtrackit.irda.club.
