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Integrity Monitoring of 3D Object Detection in Automated Driving Systems using Raw Activation Patterns and Spatial Filtering
The deep neural network (DNN) models are widely used for object detection in automated driving systems (ADS).
Run-time Monitoring of 3D Object Detection in Automated Driving Systems Using Early Layer Neural Activation Patterns
To address the real-time operation requirements in ADS, we also introduce a novel introspection method that combines activation patterns from multiple layers of the detector's backbone and report its performance.
Run-time Introspection of 2D Object Detection in Automated Driving Systems Using Learning Representations
The proposed approach pre-processes the neural activation patterns of the object detector's backbone using several different modes.
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