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Lydia Gauerhof
Lydia Gauerhof
Corporate Research, Robert Bosch GmbH
Verified email at de.bosch.com
Title
Cited by
Cited by
Year
Making the case for safety of machine learning in highly automated driving
S Burton, L Gauerhof, C Heinzemann
Computer Safety, Reliability, and Security: SAFECOMP 2017 Workshops, ASSURE …, 2017
1502017
Structuring validation targets of a machine learning function applied to automated driving
L Gauerhof, P Munk, S Burton
Computer Safety, Reliability, and Security: 37th International Conference …, 2018
632018
Assuring the safety of machine learning for pedestrian detection at crossings
L Gauerhof, R Hawkins, C Picardi, C Paterson, Y Hagiwara, I Habli
Computer Safety, Reliability, and Security: 39th International Conference …, 2020
412020
Confidence arguments for evidence of performance in machine learning for highly automated driving functions
S Burton, L Gauerhof, BB Sethy, I Habli, R Hawkins
Computer Safety, Reliability, and Security: SAFECOMP 2019 Workshops, ASSURE …, 2019
362019
Structuring the safety argumentation for deep neural network based perception in automotive applications
G Schwalbe, B Knie, T Sämann, T Dobberphul, L Gauerhof, S Raafatnia, ...
Computer Safety, Reliability, and Security. SAFECOMP 2020 Workshops: DECSoS …, 2020
272020
Testing deep learning-based visual perception for automated driving
S Abrecht, L Gauerhof, C Gladisch, K Groh, C Heinzemann, M Woehrle
ACM Transactions on Cyber-Physical Systems (TCPS) 5 (4), 1-28, 2021
212021
Facer: A universal framework for detecting anomalous operation of deep neural networks
C Schorn, L Gauerhof
2020 IEEE 23rd International Conference on Intelligent Transportation …, 2020
122020
Fault Injectors for TensorFlow: evaluation of the impact of random hardware faults on deep CNNs
M Beyer, A Morozov, E Valiev, C Schorn, L Gauerhof, K Ding, K Janschek
arXiv preprint arXiv:2012.07037, 2020
112020
Reverse variational autoencoder for visual attribute manipulation and anomaly detection
L Gauerhof, N Gu
2020 IEEE Winter Conference on Applications of Computer Vision (WACV), 2103-2112, 2020
102020
Intelligent and connected cyber-physical systems: A perspective from connected autonomous vehicles
W Chang, S Burton, CW Lin, Q Zhu, L Gauerhof, J McDermid
Intelligent Internet of Things: From Device to Fog and Cloud, 357-392, 2020
62020
Bayesian Model for Trustworthiness Analysis of Deep Learning Classifiers.
A Morozov, E Valiev, M Beyer, K Ding, L Gauerhof, C Schorn
AISafety@ IJCAI, 2020
52020
Integration of a dynamic model in a driving simulator to meet requirements of various levels of automatization
L Gauerhof, A Bilic, C Knies, F Diermeyer
2016 IEEE Intelligent Vehicles Symposium (IV), 292-297, 2016
42016
Generation of synthetic lidar signals
JN Caspers, J Ebert, L Gauerhof, M Pfeiffer, R Has, T Maurer, A Khoreva
US Patent App. 17/009,351, 2021
32021
ADAS for the communication between automated and manually driven cars
L Gauerhof, A Kürzl, M Lienkamp
7. Tagung Fahrerassistenzsysteme, 2015
32015
Automating Safety Argument Change Impact Analysis for Machine Learning Components
C Cârlan, L Gauerhof, B Gallina, S Burton
2022 IEEE 27th Pacific Rim International Symposium on Dependable Computing …, 2022
22022
Considering reliability of deep learning function to boost data suitability and anomaly detection
L Gauerhof, Y Hagiwara, C Schorn, M Trapp
2020 IEEE International Symposium on Software Reliability Engineering …, 2020
22020
Method, device, and computer program for creating training data in a vehicle
C Schorn, L Gauerhof
US Patent App. 17/658,323, 2022
12022
Method and device for training a machine learning system
L Gauerhof, N Gu
US Patent App. 17/610,669, 2022
12022
Method and device for testing the robustness of an artificial neural network
L Gauerhof, N Gu
US Patent App. 17/596,126, 2022
12022
On the necessity of explicit artifact links in safety assurance cases for machine learning
L Gauerhof, R Gansch, C Heinzemann, M Woehrle, A Heyl
2021 IEEE International Symposium on Software Reliability Engineering …, 2021
12021
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