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Strategy to Increase the Safety of a DNN-based Perception for HAD
  Systems

Strategy to Increase the Safety of a DNN-based Perception for HAD Systems

20 February 2020
Timo Sämann
Peter Schlicht
Fabian Hüger
ArXiv (abs)PDFHTML

Papers citing "Strategy to Increase the Safety of a DNN-based Perception for HAD Systems"

4 / 4 papers shown
Title
Investigating Calibration and Corruption Robustness of Post-hoc Pruned
  Perception CNNs: An Image Classification Benchmark Study
Investigating Calibration and Corruption Robustness of Post-hoc Pruned Perception CNNs: An Image Classification Benchmark Study
Pallavi Mitra
Gesina Schwalbe
Nadja Klein
AAML
82
2
0
31 May 2024
Online Out-of-Domain Detection for Automated Driving
Online Out-of-Domain Detection for Automated Driving
Timo Sämann
H. Groß
37
0
0
23 Oct 2023
Deep Learning Safety Concerns in Automated Driving Perception
Deep Learning Safety Concerns in Automated Driving Perception
Stephanie Abrecht
Alexander Hirsch
Shervin Raafatnia
Matthias Woehrle
148
16
0
07 Sep 2023
Inspect, Understand, Overcome: A Survey of Practical Methods for AI
  Safety
Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safety
Sebastian Houben
Stephanie Abrecht
Maram Akila
Andreas Bär
Felix Brockherde
...
Serin Varghese
Michael Weber
Sebastian J. Wirkert
Tim Wirtz
Matthias Woehrle
AAML
150
58
0
29 Apr 2021
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