Farzad Nozarian — accepted AI-conference papers

2024

A Safety-Adapted Loss for Pedestrian Detection in Autonomous Driving

Maria Lyssenko, Piyush Pimplikar, Maarten Bieshaar, Farzad Nozarian, Rudolph Triebel

ICRA 2024poster

In safety-critical domains like autonomous driving (AD), errors by the object detector may endanger pedestrians and other vulnerable road users (VRU). As raw evaluation metrics are not an adequate safety indicator, recent works leverage domain knowledge to identify safety-relevant VRU, and to back-a…

Cited by 1 Source ↗Scholar ↗

2022

Striving for Less: Minimally-Supervised Pseudo-Label Generation for Monocular Road Segmentation

François Robinet, Yussef Akl, Kaleem Ullah, Farzad Nozarian, Christian Müller, Raphaël Frank

RA-L 2022

Identifying traversable space is one of the most important problems in autonomous robot navigation and is primarily tackled using learning-based methods. To alleviate the prohibitively high annotation-cost associated with labeling large and diverse datasets, research has recently shifted from tradit

Cited by 0 Source ↗Scholar ↗