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ORCIDLydia Gauerhof
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2020 – today
- 2023
[b1]Lydia Gauerhof:
Essential properties for safe behaviour of a perception function in automated driving. Ludwig Maximilian University of Munich, Germany, 2023- 2022
- [c12]Carmen Cârlan, Lydia Gauerhof, Barbara Gallina, Simon Burton:
Automating Safety Argument Change Impact Analysis for Machine Learning Components. PRDC 2022: 43-53 - 2021
- [j1]Stephanie Abrecht, Lydia Gauerhof, Christoph Gladisch, Konrad Groh, Christian Heinzemann, Matthias Woehrle:
Testing Deep Learning-based Visual Perception for Automated Driving. ACM Trans. Cyber Phys. Syst. 5(4): 37:1-37:28 (2021) - [c11]Lydia Gauerhof, Roman Gansch, Christian Heinzemann, Matthias Woehrle, Andreas Heyl:
On the Necessity of Explicit Artifact Links in Safety Assurance Cases for Machine Learning. ISSRE Workshops 2021: 23-28 - 2020
- [c10]Andrey Morozov, Emil Valiev, Michael Beyer, Kai Ding, Lydia Gauerhof, Christoph Schorn:
Bayesian Model for Trustworthiness Analysis of Deep Learning Classifiers. AISafety@IJCAI 2020 - [c9]Lydia Gauerhof, Yuki Hagiwara, Christoph Schorn, Mario Trapp:
Considering Reliability of Deep Learning Function to Boost Data Suitability and Anomaly Detection. ISSRE Workshops 2020: 249-254 - [c8]Christoph Schorn, Lydia Gauerhof:
FACER: A Universal Framework for Detecting Anomalous Operation of Deep Neural Networks. ITSC 2020: 1-6 - [c7]Lydia Gauerhof, Richard Hawkins, Chiara Picardi, Colin Paterson, Yuki Hagiwara, Ibrahim Habli:
Assuring the Safety of Machine Learning for Pedestrian Detection at Crossings. SAFECOMP 2020: 197-212 - [c6]Gesina Schwalbe
, Bernhard Knie, Timo Sämann, Timo Dobberphul, Lydia Gauerhof, Shervin Raafatnia, Vittorio Rocco:
Structuring the Safety Argumentation for Deep Neural Network Based Perception in Automotive Applications. SAFECOMP Workshops 2020: 383-394 - [c5]Lydia Gauerhof, Nianlong Gu:
Reverse Variational Autoencoder for Visual Attribute Manipulation and Anomaly Detection. WACV 2020: 2103-2112 - [i1]Michael Beyer, Andrey Morozov, Emil Valiev, Christoph Schorn, Lydia Gauerhof, Kai Ding, Klaus Janschek:
Fault Injectors for TensorFlow: Evaluation of the Impact of Random Hardware Faults on Deep CNNs. CoRR abs/2012.07037 (2020)
2010 – 2019
- 2019
- [c4]Simon Burton, Lydia Gauerhof, Bibhuti Bhusan Sethy, Ibrahim Habli, Richard Hawkins:
Confidence Arguments for Evidence of Performance in Machine Learning for Highly Automated Driving Functions. SAFECOMP Workshops 2019: 365-377 - 2018
- [c3]Lydia Gauerhof, Peter Munk, Simon Burton:
Structuring Validation Targets of a Machine Learning Function Applied to Automated Driving. SAFECOMP 2018: 45-58 - 2017
- [c2]Simon Burton, Lydia Gauerhof, Christian Heinzemann:
Making the Case for Safety of Machine Learning in Highly Automated Driving. SAFECOMP Workshops 2017: 5-16 - 2016
- [c1]Lydia Gauerhof, Anito Bilic, Christian Knies, Frank Diermeyer:
Integration of a dynamic model in a driving simulator to meet requirements of various levels of automatization. Intelligent Vehicles Symposium 2016: 292-297
Coauthor Index
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