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 (Intelligent Vehicles Symposium, 2016)

Making the Case for Safety of Machine Learning in Highly Automated Driving. - Simon Burton 001; Lydia Gauerhof; Christian Heinzemann (SAFECOMP Workshops, 2017)

Structuring Validation Targets of a Machine Learning Function Applied to Automated Driving. - Lydia Gauerhof; Peter Munk; Simon Burton 001 (SAFECOMP, 2018)

Confidence Arguments for Evidence of Performance in Machine Learning for Highly Automated Driving Functions. - Simon Burton 001; Lydia Gauerhof; Bibhuti Bhusan Sethy; Ibrahim Habli; Richard Hawkins (SAFECOMP Workshops, 2019)

Bayesian Model for Trustworthiness Analysis of Deep Learning Classifiers. - Andrey Morozov 001; Emil Valiev; Michael Beyer; Kai Ding 001; Lydia Gauerhof; Christoph Schorn (AISafety@IJCAI, 2020)

Considering Reliability of Deep Learning Function to Boost Data Suitability and Anomaly Detection. - Lydia Gauerhof; Yuki Hagiwara; Christoph Schorn; Mario Trapp (ISSRE Workshops, 2020)

FACER: A Universal Framework for Detecting Anomalous Operation of Deep Neural Networks. - Christoph Schorn; Lydia Gauerhof (ITSC, 2020)

Assuring the Safety of Machine Learning for Pedestrian Detection at Crossings. - Lydia Gauerhof; Richard Hawkins; Chiara Picardi; Colin Paterson; Yuki Hagiwara; Ibrahim Habli (SAFECOMP, 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 (SAFECOMP Workshops, 2020)

Reverse Variational Autoencoder for Visual Attribute Manipulation and Anomaly Detection. - Lydia Gauerhof; Nianlong Gu (WACV, 2020)

Fault Injectors for TensorFlow: Evaluation of the Impact of Random Hardware Faults on Deep CNNs. - Michael Beyer; Andrey Morozov 001; Emil Valiev; Christoph Schorn; Lydia Gauerhof; Kai Ding 001; Klaus Janschek (CoRR, 2020)

On the Necessity of Explicit Artifact Links in Safety Assurance Cases for Machine Learning. - Lydia Gauerhof; Roman Gansch; Christian Heinzemann; Matthias Woehrle; Andreas Heyl (ISSRE Workshops, 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)

Automating Safety Argument Change Impact Analysis for Machine Learning Components. - Carmen Cârlan; Lydia Gauerhof; Barbara Gallina; Simon Burton 001 (PRDC, 2022)

Essential properties for safe behaviour of a perception function in automated driving. - Lydia Gauerhof (2023)