Roundabout Connected/Automated Vehicle Active Inference Control Strategies to Improve Safety for All Users: Phase-1 [supporting dataset]

Details

• Alternative Title:
  Video and Datasets of Roundabout Extracted Real-time Trajectories from a High Resolution Fisheye Camera supporting dataset
• Creators:
  Morris, Ted L. ; Shenoy, Varadaraya G. ; Shepard, Ben ; Morellas, Vassilios
• Corporate Creators:
  Minnesota Robotics Institute (MnRI) ; University of Minnesota, Twin Cities. Gemini-Huntley Robotics Research Laboratory
• Corporate Contributors:
  Center for Connected and Automated Transportation (CCAT) ; 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:
  • Autonomous vehicles
  • Blind spots
  • Connected vehicles
  • Deep learning
  • Detection and identification system applications
  • Roundabouts
  • Sensors
  • System architecture
  • Traffic safety
  • Data
• DOI:
  https://hdl.handle.net/11299/282868
• Resource Type:
  Dataset
• Right Statement:
  Open Access; https://creativecommons.org/licenses/by/4.0/
• Geographical Coverage:
  United States
• Corporate Publisher:
  University of Michigan. Center for Connected and Automated Transportation
• Description:
  This repository stores corresponding video and output data for the project,
  
  "Roundabout Connected/Automated Vehicle Active Inference Control Strategies to Improve Safety for All Users: Phase-I". The video is from a high resolution, 2992x2992 180-degree elevated fisheye camera deployed at the center of an urban arterial 2-lane Roundabout intersection. The footage captures all road user traffic events (Pedestrian, bicycle, public transit, and road vehicle movements) for 30 minutes, at 15 frames per second. The repository also provides the real-time traffic motion trajectory data from this video. The traffic data includes road user classification type, its temporal real-world location, speed, and heading. In addition, a data conversion python program is provided that also serves as a reference on how to input, manipulate, and save the trajectory data.
  
  The total size of the ZIP file is
• Content Notes:
  This item is made available under the terms of the Creative Commons Attribution 1.0 Universal (CC0 1.0) license https://creativecommons.org/publicdomain/zero/1.0/. Use the following citation:
• Format:
  ZIP
• Funding:
  69A3551747105
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/94151/dot_94151_DS1.md
• File Type:
  [md - 10.35 KB]
• Collection(s):
  Autonomous Vehicles ; University Transportation Centers
• Main Document Checksum:
  urn:sha-512:a3e30c1856f9caf015568ebef31eefde6b9ce1e23c0fb71b739613f2936d6c521f506b0c182f990cb4d657feaf2d18c889a7fc3c7919842c5caee787ef0e6554