Sensor Degradation Detection Algorithm for Automated Driving Systems

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Date

2023-03

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Safe-D National UTC

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.

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Keywords

algorithms, degradation failures, detection and identification systems, driver support systems, Global Positioning System, laser radar, metrics (quantitative assessment), sensors

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