Developing Systems for Autonomous Vehicles to Identify and Localize Emergency Vehicles

Within the CAST (Connected Autonomous Safe Transportation) Program, one of the projects is exploration to determine how an autonomous vehicle should respond to emergency vehicles on the road. There are three threads of investigations: 1. Determination of the “right” or “ideal” response. The researchers are interacting with the TEEX Law Enforcement and Security Training, and through them with local police and fire department personnel, to identify key traffic scenarios involving emergency vehicles, and the possible sets of good and bad responses from an autonomous vehicle. 2. Identification and possible Localization of an Emergency Vehicle. This would be a combination of detection and identification of sound and visual signals from an Emergency Vehicle. The researchers will primarily be evaluating different AI/Machine Learning techniques. 3. Response execution based on the identification of an Emergency Vehicle. The researchers already have an autonomous vehicle within CAST that can be programmed as desired. The researchers will be implementing and demonstrating the desired emergency response on this vehicle. During the summer 2017, students assigned to this project will complete the following tasks: 1. Investigate the use of various machine learning techniques to process sound data, and identify emergency vehicles. The sound signals were processed to extract several key sound features, which were then used as the basis for performing identification of the type of the sound signal. Learning models were trained offline using a database of different sound signals to perform the identification. Then these training models were adapted for real-time application to sound signals captured directly from microphones. The performance of these identification algorithms was verified using real microphones listening to recorded emergency vehicle sounds. 2. Begin experimentations, in collaboration with TEEX law Enforcement and Security Training, to recreate typical traffic scenarios of interest in the context of emergency vehicles. Specifically, the research team performed experiments where an autonomous vehicle was instrumented with multiple microphones and cameras, and two emergency vehicles made simulated runs with sirens on and emergency lights flashing. This data has been collected and is being processed. The end-goal of the summer work is to lead to a formal proposal for sustained research into this important safety consideration for autonomous vehicles, namely, how should the autonomous vehicles respond to emergency vehicles on the road. Further the team is preparing to make a conference paper derived from the summer work.


  • English


  • Status: Active
  • Contract Numbers:


  • 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:

    Safety through Disruption University Transportation Center

    Virginia Tech Transportation Institute
    Blacksburg, VA  United States  24060
  • Project Managers:

    Harwood, Leslie

  • Performing Organizations:

    Texas A&M Transportation Institute

    Texas A&M University System
    3135 TAMU
    College Station, TX  United States  77843-3135
  • Principal Investigators:

    Rathinam, Sivakumar

  • Start Date: 20170701
  • Expected Completion Date: 20170831
  • Actual Completion Date: 0
  • USDOT Program: University Transportation Centers Program

Subject/Index Terms

Filing Info

  • Accession Number: 01658906
  • Record Type: Research project
  • Source Agency: Safety through Disruption University Transportation Center
  • Contract Numbers: 69A3551747115
  • Files: UTC, RiP
  • Created Date: Feb 2 2018 8:26PM