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Found 7 papers, 1 papers with code
Evaluation of Out-of-Distribution Detection Performance on Autonomous Driving Datasets
Safety measures need to be systemically investigated to what extent they evaluate the intended performance of Deep Neural Networks (DNNs) for critical applications.
Performance Analysis of Out-of-Distribution Detection on Trained Neural Networks
Implementing Deep Neural Networks (DNN) for non-safety related applications have shown remarkable achievements over the past years; however, for using DNNs in safety critical applications, we are missing approaches for verifying the robustness of such models.
Understanding the Impact of Edge Cases from Occluded Pedestrians for ML Systems
As the NN is trained on well annotated images, in this paper we study the variations of confidence levels from the NN when tested on hand-crafted occlusion added to a test set.
Ergo, SMIRK is Safe: A Safety Case for a Machine Learning Component in a Pedestrian Automatic Emergency Brake System
We initiated a research project with the goal to demonstrate a complete safety case for an ML component in an open automotive system.
Performance Analysis of Out-of-Distribution Detection on Various Trained Neural Networks
Understanding the relationship between training results and supervisor performance is valuable to improve robustness of the model and indicates where more work has to be done to create generalized models for safety critical applications.
Controlled time series generation for automotive software-in-the-loop testing using GANs
Testing automotive mechatronic systems partly uses the software-in-the-loop approach, where systematically covering inputs of the system-under-test remains a major challenge.
Towards Structured Evaluation of Deep Neural Network Supervisors
Deep Neural Networks (DNN) have improved the quality of several non-safety related products in the past years.
