Reliable Multimodal Trajectory Prediction via Error Aligned Uncertainty Optimization

no code implementations • 9 Dec 2022 • Neslihan Kose, Ranganath Krishnan, Akash Dhamasia, Omesh Tickoo, Michael Paulitsch

Reliable uncertainty quantification in deep neural networks is very crucial in safety-critical applications such as automated driving for trustworthy and informed decision-making.

Decision Making motion prediction +3

Towards a Safety Case for Hardware Fault Tolerance in Convolutional Neural Networks Using Activation Range Supervision

no code implementations • 16 Aug 2021 • Florian Geissler, Syed Qutub, Sayanta Roychowdhury, Ali Asgari, Yang Peng, Akash Dhamasia, Ralf Graefe, Karthik Pattabiraman, Michael Paulitsch

Convolutional neural networks (CNNs) have become an established part of numerous safety-critical computer vision applications, including human robot interactions and automated driving.