A Sensor Fusion and Localization System for Improving Vehicle Safety In Challenging Weather Conditions

The safety of autonomous\connected vehicles primarily relies on their ability to accurately sense the environment. The sensing problem is significantly challenging in weather conditions which include sudden change in lighting, smoke, fog, snow, and rain. Therefore, accurate sensing in adverse weather conditions is a critical and important safety problem that needs to be solved for any successful deployment of autonomous vehicles. There is currently no single sensor currently available in the market that can handle all the possible performance aspects and adverse weather conditions. The objective of this project is to use a combination of Radars and FIR cameras in addition to a LIDAR based system to map the environment and localize the vehicle with respect to the lanes on the road. This project will develop a prototype of an all weather sensing and localization system which will be useful for any autonomous or connected vehicle. The performance of the developed system will be corroborated with several data sets collected at Rellis. Demonstrations of the developed technology will also be done at the yearly Automated Vehicle Symposium, and SAE conferences.

• Record URL:
  https://www.vtti.vt.edu/utc/safe-d/index.php/projects/a-sensor-fusion-and-localization-system-for-improving-vehicle-safety-in-challenging-weather-conditions/

Language

• English

Project

• Status: Active
• Funding: $190409
• Contract Numbers:

  69A3551747115

• 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 (Safe-D)

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

  Glenn, Eric

• Performing Organizations:

  Texas A&M Transportation Institute, College Station

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

  Rathinam, Sivakumar

• Start Date: 20190501
• Expected Completion Date: 20211031
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers Program

Subject/Index Terms

• TRT Terms: Autonomous vehicles; Connected vehicles; Laser radar; Location; Prototypes; Sensors; Vehicle safety; Weather conditions
• Subject Areas: Data and Information Technology; Design; Highways; Safety and Human Factors; Vehicles and Equipment;

Filing Info

• Accession Number: 01701522
• Record Type: Research project
• Source Agency: Safety through Disruption University Transportation Center (Safe-D)
• Contract Numbers: 69A3551747115
• Files: UTC, RIP
• Created Date: Apr 8 2019 4:12PM