Sensor Degradation Detection Algorithm for Automated Driving Systems

Principal Investigator: 
Michelle Chaka, program director, Virginia Tech Transportation Institute

Co-Principal Investigators: 
Chunsheng Xin, professor, Center for Cybersecurity Education and Research, electrical and computer engineering, Old Dominion University; Kevin Kefauver, technical director, Global Center for Automotive Performance Simulation

Project Description: 
The project will develop a sensor degradation detection algorithm for Automated Driving Systems (ADS). Sources of degraded sensor information include weather, cyberattacks (e.g., direct communication and passive false signage), and sensor malfunction. Incorrect information from a sensor could result in significant safety issues, such as leading the vehicle off the road or causing the vehicle to suddenly stop in the middle of an intersection. From the Virginia Tech Transportation Institute’s (VTTI’s) Naturalistic Driving Database (NDD), 1000 events related to sensor perception will be selected to establish baseline sensor performance. VTTI will then determine performance metrics using these events extracted from the NDD for comparison in simulation. A virtual framework will be used to test degraded sensor states and the response of the vehicle control systems to develop the detection algorithm. The framework will integrate the sensors models, environments, vehicle model, cyberattacks, and algorithm. Old Dominion University will develop the GPS model, which is a localization sensor, and collaborate with the Global Center for Automotive Performance Simulation (GCAPS) to develop the degradation detection algorithm. GCAPS will also create the virtual framework, develop the LiDAR and radar sensor models, and execute the simulations. The sensor degradation detection algorithm will aid ADS vehicles in decision making by identifying degraded sensor performance.

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