Generating High-Accuracy Transportation Datasets with Unmanned Aerial Vehicles

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 shared with CAVs via V2X connectivity. This has a huge potential to improve traffic safety and efficiency. The study team identifies 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.

• Record URL:
  https://ccat.umtri.umich.edu/research/u-m/generating-high-accuracy-transportation-datasets-with-unmanned-aerial-vehicles/

Language

• English

Project

• Status: Active
• Funding: $120,000
• Contract Numbers:

  69A3552348305

• Sponsor Organizations:

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590
• Managing Organizations:

  University of Michigan Transportation Research Institute

  2901 Baxter Road
  Ann Arbor, Michigan  United States  48109
• Project Managers:

  Stearns, Amy

• Performing Organizations:

  University of Michigan, Ann Arbor

  Department of Civil and Environmental Engineering
  2350 Hayward
  Ann Arbor, MI  United States  48109-2125
• Principal Investigators:

  Orosz, Gabor

• Start Date: 20231201
• Expected Completion Date: 20241130
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers

Subject/Index Terms

• TRT Terms: Autonomous vehicles; Connected vehicles; Data quality; Data sharing; Drones; Machine learning; Traffic safety; Traffic surveillance; Unmanned aircraft systems; Vehicle to infrastructure communications; Vehicle to vehicle communications
• Subject Areas: Data and Information Technology; Highways; Safety and Human Factors; Vehicles and Equipment;

Filing Info

• Accession Number: 01906129
• Record Type: Research project
• Source Agency: Center for Connected and Automated Transportation
• Contract Numbers: 69A3552348305
• Files: UTC, RIP
• Created Date: Jan 26 2024 4:45PM