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Artificial intelligence solutions for Autonomous Vehicles (AVs) have beendeveloped using publicly available datasets such as Argoverse, ApolloScape,Level5, and NuScenes. One major limitation of these datasets is the absence ofinfrastructure and/or pooled vehicle information like lane line type, vehiclespeed, traffic signs, and intersections. Such information is necessary and notcomplementary to eliminating high-risk edge cases. The rapid advancements inVehicle-to-Infrastructure and Vehicle-to-Vehicle technologies show promise thatinfrastructure and pooled vehicle information will soon be accessible in nearreal-time. Taking a leap in the future, we introduce the first comprehensivesynthetic dataset with intelligent infrastructure and pooled vehicleinformation for advancing the next generation of AVs, named VTrackIt. We alsointroduce the first deep learning model (InfraGAN) for trajectory predictionsthat considers such information. Our experiments with InfraGAN show that thecomprehensive information offered by VTrackIt reduces the number of high-riskedge cases. The VTrackIt dataset is available upon request under the CreativeCommons CC BY-NC-SA 4.0 license at http://vtrackit.irda.club.

VTrackIt: A Synthetic Self-Driving Dataset with Infrastructure and Pooled Vehicle Information