Lydia Gauerhof
Lydia Gauerhof
Corporate Research, Robert Bosch GmbH
Bestätigte E-Mail-Adresse bei
Zitiert von
Zitiert von
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
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
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
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
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
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
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
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
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
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
Bayesian Model for Trustworthiness Analysis of Deep Learning Classifiers.
A Morozov, E Valiev, M Beyer, K Ding, L Gauerhof, C Schorn
AISafety@ IJCAI, 2020
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
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
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
ADAS for the communication between automated and manually driven cars
L Gauerhof, A Kürzl, M Lienkamp
7. Tagung Fahrerassistenzsysteme, 2015
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
Method, device, and computer program for creating training data in a vehicle
C Schorn, L Gauerhof
US Patent App. 17/658,323, 2022
Method and device for training a machine learning system
L Gauerhof, N Gu
US Patent App. 17/610,669, 2022
Method and device for testing the robustness of an artificial neural network
L Gauerhof, N Gu
US Patent App. 17/596,126, 2022
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
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