Visibility Guided NMS: Efficient Boosting of Amodal Object Detection in Crowded Traffic Scenes. - Nils Gählert; Niklas Hanselmann; Uwe Franke; Joachim Denzler (CoRR, 2020)

Learning Cascaded Detection Tasks with Weakly-Supervised Domain Adaptation. - Niklas Hanselmann; Nick Schneider; Benedikt Ortelt; Andreas Geiger 001 (IV, 2021)

KING: Generating Safety-Critical Driving Scenarios for Robust Imitation via Kinematics Gradients. - Niklas Hanselmann; Katrin Renz; Kashyap Chitta; Apratim Bhattacharyya; Andreas Geiger 001 (ECCV (38), 2022)

Unsupervised Domain Adaptive Object Detection with Class Label Shift Weighted Local Features. - Andong Tan; Niklas Hanselmann; Shuxiao Ding; Federico Tombari; Marius Cordts (ECCV Workshops (2), 2022)

PowerBEV: A Powerful Yet Lightweight Framework for Instance Prediction in Bird's-Eye View. - Peizheng Li; Shuxiao Ding; Xieyuanli Chen; Niklas Hanselmann; Marius Cordts; Juergen Gall (IJCAI, 2023)

Dualad: Disentangling the Dynamic and Static World for End-to-End Driving. - Simon Doll; Niklas Hanselmann; Lukas Schneider; Richard Schulz; Marius Cordts; Markus Enzweiler; Hendrik P. A. Lensch (CVPR, 2024)

S.T.A.R.-Track: Latent Motion Models for End-to-End 3D Object Tracking With Adaptive Spatio-Temporal Appearance Representations. - Simon Doll; Niklas Hanselmann; Lukas Schneider; Richard Schulz; Markus Enzweiler; Hendrik P. A. Lensch (IEEE Robotics Autom. Lett., 2024)

AGO: Adaptive Grounding for Open World 3D Occupancy Prediction. - Peizheng Li; Shuxiao Ding; You Zhou; Qingwen Zhang; Onat Inak; Larissa Triess; Niklas Hanselmann; Marius Cordts; Andreas Zell (CoRR, 2025)

Emperror: A Flexible Generative Perception Error Model for Probing Self-Driving Planners. - Niklas Hanselmann; Simon Doll; Marius Cordts; Hendrik P. A. Lensch; Andreas Geiger 001 (IEEE Robotics Autom. Lett., 2025)