Abstract
The project developed a sensor degradation detection algorithm for Automated Driving Systems (ADS). Weather, cyberattacks, and sensor malfunction can degrade sensor information, resulting in significant safety issues, such as leading the vehicle off the road or causing a sudden stop in the middle of an intersection. From the Virginia Tech Transportation Institute’s (VTTI’s) Naturalistic Driving Database (NDD), 100 events related to sensor perception were selected to establish baseline sensor performance. VTTI determined performance metrics using these events for comparison in simulation. A virtual framework was used to test degraded sensor states and the detection algorithm’s response. Old Dominion University developed the GPS model and collaborated with the Global Center for Automotive Performance Simulation (GCAPS) to develop the degradation detection algorithm utilizing the DeepPOSE algorithm. GCAPS created the virtual framework, developed the LiDAR and radar sensor models, and executed the simulations. The sensor degradation detection algorithm will aid ADS vehicles in decision making by identifying degraded sensor performance. The detection algorithm achieved 70% accuracy. Additional training methods and adjustments are needed for the accuracy level required for vehicle system implementation. The process of collecting sensor data, creating sensor models, and utilizing simulation for algorithm development are major outcomes of the research.
Project Highlights
- Created a method to train a sensor degradation detection algorithm through simulation, specifically for characterizing and implementing degraded sensors.
- Outreach included a presentation at the Road Safety on Five Continents (RS5C) conference and a USDOT UTC RD&T Video Webinar on Cybersecurity and Transportation.
Final Report
VTTI-00-034 Final Research Report
EWD & T2 Products
Student Impact Statement(pdf): One students received funding under this project (Peng Jiang, a PhD student from Old Dominion University). This file contains a statement by Peng Jiang as to the impact this project had on education and workforce development.
Huggins, S. (2022, October 12). ADS Sensor Degradation Testing, Modeling, and Detection [Conference presentation]. Road Safety on Five Continents 2022 Conference, Grapevine, TX, United States. Presentation Available here.
USDOT UTC RD&T Video Webinar on Cybersecurity and Transportation on 7 March 2023.
“Lightning Lecture” on CCI website.
Presentations/Publications
Huggins, S. (2022, August 19). ADS Sensor Degradation Testing, Modeling, and Detection. Webinar SafeD Virtual Webinar is available here. PDF of PowerPoint slides from Webinar available here.
Final Dataset
The final datasets for this project are located in the Safe-D Collection on the VTTI Dataverse; DOI: 10.15787/VTT1/QXLEMM.
Research Investigators (PI*)
Christina Witcher (VTTI/VT)*
Kevin Kefauver (GCAPS)*
ChunSheng Xin (ODU)
Peng Jiang (ODU-Student)
Jonathan Darab (GCAPS)
Alex Hatchett (GCAPS)
Kenny Custer (GCAPS)
Stephen Young (GCAPS)
Cong Chen (GCAPS)
Steven Huggins (GCAPS)
Project Information
Start Date: 2021-04-01
End Date: 2022-08-31
Status: Completed
Grant Number: 69A3551747115
Total Funding: $400,000
Source Organization: Safe-D National UTC
Project Number: VTTI-00-034
Safe-D Theme Areas
Safe-D Application Areas
Risk Assessment
Driver Factors and Influences
Performance Measures
Planning for Safety
More Information
Sponsor Organization
Office of the Assistant Secretary for Research and Technology
University Transportation Centers Program
Department of Transportation
Washington, DC 20590 United States
Performing Organization
Virginia Polytechnic Institute and State University
Virginia Tech Transportation Institute
3500 Transportation Research Plaza
Blacksburg, Virginia 24061
USA