Lydia Gauerhof
Lydia Gauerhof
Corporate Research, Robert Bosch GmbH
Verifierad e-postadress på de.bosch.com
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Making the case for safety of machine learning in highly automated driving
S Burton, L Gauerhof, C Heinzemann
International Conference on Computer Safety, Reliability, and Security, 5-16, 2017
702017
Structuring validation targets of a machine learning function applied to automated driving
L Gauerhof, P Munk, S Burton
International Conference on Computer Safety, Reliability, and Security, 45-58, 2018
292018
Confidence arguments for evidence of performance in machine learning for highly automated driving functions
S Burton, L Gauerhof, BB Sethy, I Habli, R Hawkins
International Conference on Computer Safety, Reliability, and Security, 365-377, 2019
172019
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
52020
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, 357-392, 2020
42020
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
Assuring the safety of machine learning for pedestrian detection at crossings
L Gauerhof, R Hawkins, C Picardi, C Paterson, Y Hagiwara, I Habli
International Conference on Computer Safety, Reliability, and Security, 197-212, 2020
32020
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
12020
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
2020
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
2020
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, ...
International Conference on Computer Safety, Reliability, and Security, 383-394, 2020
2020
Structuring the Safety Argumentation for Deep Neural Network Based Perception in Automotive Applications
L Gauerhof, S Raafatnia, V Rocco
Computer Safety, Reliability, and Security: SAFECOMP 2020 Workshops: DECSoS …, 2020
2020
Ein Sicherheitsnachweis fuer Machine-Learning im Kontext des automatisierten Fahrens
S BURTON, L Gauerhof, C Heinzemann, L BUERKLE
Haus der Technik, 2018
2018
ADAS for the Communication between Automated and Manually Driven Cars
L Gauerhof, A Kürzl, M Lienkamp
7. Tagung Fahrerassistenz, 2015
2015
Bayesian Model for Trustworthiness Analysis of Deep Learning Classifiers
A Morozov, E Valiev, M Beyer, K Ding, L Gauerhof, C Schorn
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Artiklar 1–15