19 citations · 32 across the 4 of their papers we have counts for
4 papers
Reliable Multimodal Trajectory Prediction via Error Aligned Uncertainty Optimization
Neslihan Kose, Ranganath Krishnan, Akash Dhamasia +2
Reliable uncertainty quantification in deep neural networks is very crucial in safety-critical applications such as automated driving for trustworthy and informed decision-making.…
Hardware faults that matter: Understanding and Estimating the safety impact of hardware faults on object detection DNNs
Syed Qutub, Florian Geissler, Yang Peng +4
Object detection neural network models need to perform reliably in highly dynamic and safety-critical environments like automated driving or robotics. Therefore, it is paramount to…
Towards a Safety Case for Hardware Fault Tolerance in Convolutional Neural Networks Using Activation Range Supervision
Florian Geissler, Syed Qutub, Sayanta Roychowdhury +6
Convolutional neural networks (CNNs) have become an established part of numerous safety-critical computer vision applications, including human robot interactions and automated driv…
A Plausibility-based Fault Detection Method for High-level Fusion Perception Systems
Florian Geissler, Alex Unnervik, Michael Paulitsch
Trustworthy environment perception is the fundamental basis for the safe deployment of automated agents such as self-driving vehicles or intelligent robots. The problem remains tha…