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

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

Custom AI Architectures for Predictive Analytics Using Bayesian Statistics and Deep Learning

Predictive analytics has emerged as a vital field with significant potential in industries ranging from energy to mobility. As such, it has become a topic of considerable interest for research. Through the use of statistical models, predictive analytics helps reveal patterns and relationships in complex datasets, generating accurate predictions about future events or outcomes. The development of Artificial Intelligence (AI) architectures and data-driven frameworks has further revolutionized the way we perform predictive analytics. However, the broad adoption of AI for predictive analytics is limited due to the lack of custom architectures that can effectively handle the unique complexities of modern datasets and perform robust and accurate predictions. As datasets grow increasingly complex, the need for Bayesian statistics and Deep Learning (DL) in predictive analytics has become increasingly evident. Bayesian statistics offers a versatile framework for incorporating prior knowledge and external knowledge into AI models. This can help mitigate problems such as data sparsity and improve long-term forecasts. Similarly, DL architectures, with their ability to identify and learn complex patterns within datasets, have the potential to unlock new insights and drive innovation in predictive analytics. However, the development of custom AI architectures that leverage such techniques for predictive analytics remains challenging due to their several inherent limitations. This work aims to bridge this research gap by harnessing the power of Bayesian statistics and DL to advance the state-of-the-art in predictive analytics. Specifically, this work proposes custom AI architectures and data-driven frameworks that can (i) perform accurate long-term estimations, (ii) overcome data drift, (iii) provide uncertainty quantifications, (iv) model and predict anomalous behavior, (v) leverage concepts of Design of Experiments, and (vi) perform collaborative modeling. The proposed models and frameworks are evaluated using compelling case studies that demonstrate their effectiveness in improving the accuracy, reliability, and robustness of AI architectures for broader use in predictive analytics.Ph.D.College of Engineering & Computer ScienceUniversity of Michigan-Dearbornhttp://deepblue.lib.umich.edu/bitstream/2027.42/175992/1/Mayuresh Savargaonkar Final Dissertation.pdfDescription of Mayuresh Savargaonkar Final Dissertation.pdf : Dissertatio