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Runtime Monitoring DNN-Based Perception
Deep neural networks (DNNs) are instrumental in realizing complex perception systems.
Monitoring Object Detection Abnormalities via Data-Label and Post-Algorithm Abstractions
While object detection modules are essential functionalities for any autonomous vehicle, the performance of such modules that are implemented using deep neural networks can be, in many cases, unreliable.
Testing Autonomous Systems with Believed Equivalence Refinement
Continuous engineering of autonomous driving functions commonly requires deploying vehicles in road testing to obtain inputs that cause problematic decisions.
Continuous Safety Verification of Neural Networks
Deploying deep neural networks (DNNs) as core functions in autonomous driving creates unique verification and validation challenges.
Architecting Dependable Learning-enabled Autonomous Systems: A Survey
We provide a summary over architectural approaches that can be used to construct dependable learning-enabled autonomous systems, with a focus on automated driving.
