Uncertainty Quantification and Calibration of Imitation Learning Policy in Autonomous Driving. - Farzad Nozarian; Christian Müller 014; Philipp Slusallek (TAILOR, 2020)

Striving for Less: Minimally-Supervised Pseudo-Label Generation for Monocular Road Segmentation. - François Robinet; Yussef Akl; Kaleem Ullah; Farzad Nozarian; Christian Müller 014; Raphaël Frank (IEEE Robotics Autom. Lett., 2022)

Reliable Student: Addressing Noise in Semi-Supervised 3D Object Detection. - Farzad Nozarian; Shashank Agarwal; Farzaneh Rezaeianaran; Danish Shahzad; Atanas Poibrenski; Christian Müller 014; Philipp Slusallek (CVPR Workshops, 2023)

Uncertainty-Aware Pseudo Labels for Domain Adaptation in Pedestrian Trajectory Prediction. - Atanas Poibrenski; Farzad Nozarian; Farzaneh Rezaeianaran; Christian Müller 014 (ITSC, 2023)

A Safety-Adapted Loss for Pedestrian Detection in Autonomous Driving. - Maria Lyssenko; Piyush Pimplikar; Maarten Bieshaar; Farzad Nozarian; Rudolph Triebel (ICRA, 2024)

A Safety-Adapted Loss for Pedestrian Detection in Automated Driving. - Maria Lyssenko; Piyush Pimplikar; Maarten Bieshaar; Farzad Nozarian; Rudolph Triebel (CoRR, 2024)