论文标题
VTRACKIT：具有基础架构和合并车辆信息的合成自动驾驶数据集
VTrackIt: A Synthetic Self-Driving Dataset with Infrastructure and Pooled Vehicle Information
论文作者
论文摘要
使用Argoverse，Apolloscape，Level5和Nuscenes之类的公开数据集开发了用于自动驾驶汽车（AV）的人工智能解决方案。这些数据集的一个主要局限性是缺乏基础架构和/或合并的车辆信息，例如车道线类型，车辆速度，交通标志和交叉点。此类信息是必要的，对于消除高风险边缘案例而不是补充。车辆到基础设施和车辆到车辆技术的快速进步表明，基础设施和合并的车辆信息很快将很快在实时近乎实时访问。将来飞跃，我们介绍了第一个具有智能基础架构的全面合成数据集，并汇总了车辆信息，以推动下一代AVS（名为Vtrackit）。我们还介绍了考虑此类信息的轨迹预测的第一个深度学习模型（Infragan）。我们对Infragan进行的实验表明，Vtrackit提供的全面信息减少了高风险边缘案例的数量。 vtrackit数据集可应http://vtrackit.irda.club的创意共享CC BY-NC-SA 4.0许可根据要求提供。
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.
