Generating High-Accuracy Transportation Datasets with Unmanned Aerial Vehicles

Details

• Creators:
  Köpeczi-Bócz, Ákos T. ; Mi, Tian ; Takács, Dénes ; Orosz, Gábor
• Corporate Creators:
  Center for Connected and Automated Transportation (CCAT) ; University of Michigan. Department of Mechanical Engineering
• Corporate Contributors:
  United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology
• Subject/TRT Terms:
  • Datasets
  • Autonomous vehicles
  • Drones
  • Connected vehicles
  • Image processing
  • Video cameras
  • Machine learning
  • Global Positioning System
  • Vehicle localization
  • infrastructure-based perception
  More +
• Publication/ Report Number:
  89
• DOI:
  https://doi.org/10.7302/dspace/30322
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Edition:
  Final Report (June 2023 - August 2026)
• Corporate Publisher:
  University of Michigan. Center for Connected and Automated Transportation
• Abstract:
  A new trend in the area of connected and automated vehicles (CAVs) is infrastructure-based sensing and tracking. By installing roadside cameras at the infrastructure, one can monitor the overall traffic situation and, after processing the collected data via edge computing, the information can be communicated to CAVs via V2X connectivity. This has a huge potential to improve traffic safety and efficiency. We identify two main challenges to deploy such systems: (i) to obtain high precision data and (ii) to label the obtained data. It is necessary to have sufficient high-precision labelled datasets for training the underlying machine learning (ML) algorithms to detect, identify, localize and track the road participants. In other words, one needs to know the "ground truth" (with high precision)for large variety of different scenarios. As of now, this may be done by hand-labeling images, which is immensely labor intensive, or by using probe vehicles equipped with high precision GPS, which can only provide data about a few specific vehicles.
• Format:
  PDF
• Funding:
  69A3552348305
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/93413/dot_93413_DS1.pdf
• File Type:
  [PDF - 1.64 MB]
• Collection(s):
  Autonomous Vehicles ; University Transportation Centers
• Main Document Checksum:
  urn:sha-512:0fee1403fa6aec8f255e98da7da0357e13fee4e556b7cd303b9b0d5a98c63e19930bddb09fb3ce40616583cc024ef193540e1e45d6f6c663a0fefeb87a7a8c63