Response of Autonomous Vehicles to Emergency Response Vehicles (03-051) [supporting dataset]

Project Description: The objective of this project is to explore how an autonomous vehicle must identify and safely respond to emergency vehicles using sound, vision and other onboard sensors. Emergency vehicles can belong to police, fire, hospital and other responders. The autonomous vehicle in the presence of an emergency vehicle must have the ability to accurately sense its surroundings in real-time and be able to safely yield to the emergency vehicles. This project identifies the presence of emergency vehicle mainly through onboard vision and safely maneuvers the vehicle and stops it. The project also explored using onboard sound sensors to identify the emergency vehicles. Several proof of concept demonstrations were performed, and the results were presented in several conferences and in poster sessions. The project also lead to the publication of a conference article and fully/partly supported the thesis of an M.S. and several Ph.D. students. Data Scope: Audio Dataset: The dataset used to train the machine learning algorithms for audio based EV detection include a large collection of audio files pertaining to emergency and non-emergency vehicles as well as other road sounds. The audio dataset is annotated into 527 classes representative of various sounds. The data is processed to extract audio files relevant to emergency and non-emergency classes by extracting features like zero crossing rate, spectral parameters, chromatograms etc., using the PyAudio Analysis library. Emergency Vehicle videos: Videos of Emergency vehicles collected from inside the Autonomous vehicle using BFLY-PGE-20E4C-CS 1/1.8" Blackfly® PoE GigE Color Camera. Classification Model Weights: Training weights generated by machine learning models developed and trained on the RAVEV dataset. The different combinations of parameters are as listed in ‘Nayak, A., Gopalswamy, S., and Rathinam, S., "Vision-Based Techniques for Identifying Emergency Vehicles," SAE Technical Paper 2019-01-0889, 2019, https://doi.org/10.4271/2019-01-0889’.

• Dataset URL:
  http://doi.org/10.15787/VTT1/IVNW9L
• Record URL:
  https://safed.vtti.vt.edu/wp-content/uploads/2020/08/03-051_FinalResearchReport_Final.pdf
• Supplemental Notes:
  • The dataset supports report: Response of Autonomous Vehicles to Emergency Response Vehicles (RAVEV), available at the URL above. This document was sponsored by the U.S. Department of Transportation, University Transportation Centers Program.
• Corporate Authors:

  Safety Through Disruption University Transportation Center (Safe-D)

  Texas A&M Transportation Institute
  College Station, TX  United States 

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590
• Authors:
  • Nayak, Abhishek
  • 0000-0003-0371-7747
  • Rathinam, Sivakumar
  • 0000-0002-9223-7456
  • Gopalswamy, Swaminathan
  • 0000-0003-3150-4589
• Publication Date: 2019-9-12

Language

• English

Media Info

• Media Type: Dataset
• Dataset: Version: 1.0 Integrity Hash:
• Dataset publisher:

  Virginia Tech Transportation Institute Dataverse

  ,    

Subject/Index Terms

• TRT Terms: Algorithms; Audio media; Autonomous vehicles; Data; Detection and identification systems; Emergency vehicles; Machine learning; Sensors; Sound; Video
• Subject Areas: Data and Information Technology; Highways; Vehicles and Equipment;

Filing Info

• Accession Number: 01775976
• Record Type: Publication
• Contract Numbers: 69A3551747115/Project 03-051
• Files: UTC, TRIS, ATRI, USDOT
• Created Date: Jul 6 2021 2:18PM