Lydia Gauerhof

According to our database, Lydia Gauerhof authored at least 15 papers between 2016 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2023

Essential properties for safe behaviour of a perception function in automated driving.

Lydia Gauerhof

PhD thesis, 2023

2022

Automating Safety Argument Change Impact Analysis for Machine Learning Components.

Carmen Cârlan, Lydia Gauerhof, Barbara Gallina, Simon Burton

Proceedings of the 27th IEEE Pacific Rim International Symposium on Dependable Computing, 2022

2021

Testing Deep Learning-based Visual Perception for Automated Driving.

Stephanie Abrecht, Lydia Gauerhof, Christoph Gladisch, Konrad Groh, Christian Heinzemann, Matthias Woehrle

ACM Trans. Cyber Phys. Syst., 2021

On the Necessity of Explicit Artifact Links in Safety Assurance Cases for Machine Learning.

Lydia Gauerhof, Roman Gansch, Christian Heinzemann, Matthias Woehrle, Andreas Heyl

Proceedings of the IEEE International Symposium on Software Reliability Engineering, 2021

2020

Fault Injectors for TensorFlow: Evaluation of the Impact of Random Hardware Faults on Deep CNNs.

Michael Beyer, Andrey Morozov, Emil Valiev, Christoph Schorn, Lydia Gauerhof, Kai Ding, Klaus Janschek

CoRR, 2020

Reverse Variational Autoencoder for Visual Attribute Manipulation and Anomaly Detection.

Lydia Gauerhof, Nianlong Gu

Proceedings of the IEEE Winter Conference on Applications of Computer Vision, 2020

Structuring the Safety Argumentation for Deep Neural Network Based Perception in Automotive Applications.

Gesina Schwalbe, Bernhard Knie, Timo Sämann, Timo Dobberphul, Lydia Gauerhof, Shervin Raafatnia, Vittorio Rocco

Proceedings of the Computer Safety, Reliability, and Security. SAFECOMP 2020 Workshops, 2020

Assuring the Safety of Machine Learning for Pedestrian Detection at Crossings.

Lydia Gauerhof, Richard Hawkins, Chiara Picardi, Colin Paterson, Yuki Hagiwara, Ibrahim Habli

Proceedings of the Computer Safety, Reliability, and Security, 2020

FACER: A Universal Framework for Detecting Anomalous Operation of Deep Neural Networks.

Christoph Schorn, Lydia Gauerhof

Proceedings of the 23rd IEEE International Conference on Intelligent Transportation Systems, 2020

Considering Reliability of Deep Learning Function to Boost Data Suitability and Anomaly Detection.

Lydia Gauerhof, Yuki Hagiwara, Christoph Schorn, Mario Trapp

Proceedings of the 2020 IEEE International Symposium on Software Reliability Engineering Workshops, 2020

Bayesian Model for Trustworthiness Analysis of Deep Learning Classifiers.

Andrey Morozov, Emil Valiev, Michael Beyer, Kai Ding, Lydia Gauerhof, Christoph Schorn

Proceedings of the Workshop on Artificial Intelligence Safety 2020 co-located with the 29th International Joint Conference on Artificial Intelligence and the 17th Pacific Rim International Conference on Artificial Intelligence (IJCAI-PRICAI 2020), 2020

2019

Confidence Arguments for Evidence of Performance in Machine Learning for Highly Automated Driving Functions.

Simon Burton, Lydia Gauerhof, Bibhuti Bhusan Sethy, Ibrahim Habli, Richard Hawkins

Proceedings of the Computer Safety, Reliability, and Security, 2019

2018

Structuring Validation Targets of a Machine Learning Function Applied to Automated Driving.

Lydia Gauerhof, Peter Munk, Simon Burton

Proceedings of the Computer Safety, Reliability, and Security, 2018

2017

Making the Case for Safety of Machine Learning in Highly Automated Driving.

Simon Burton, Lydia Gauerhof, Christian Heinzemann

Proceedings of the Computer Safety, Reliability, and Security, 2017

2016

Integration of a dynamic model in a driving simulator to meet requirements of various levels of automatization.

Lydia Gauerhof, Anito Bilic, Christian Knies, Frank Diermeyer

Proceedings of the 2016 IEEE Intelligent Vehicles Symposium, 2016