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Niklas Hanselmann

7 accepted papers

2026

EMPERROR: A Flexible Generative Perception Error Model for Probing Self-Driving Planners

ICRA 2026poster

To handle the complexities of real-world traffic, learning planners for self-driving from data is a promising direction. While recent approaches have shown great progress, they typically assume a setting in which the ground-truth world state is available as input. However, when deployed, planning ne…

2025

AGO: Adaptive Grounding for Open World 3D Occupancy Prediction

ICCV 2025poster

Open-world 3D semantic occupancy prediction aims to generate a voxelized 3D representation from sensor inputs while recognizing both known and unknown objects. Transferring open-vocabulary knowledge from vision-language models (VLMs) offers a promising direction but remains challenging. However, met…

2025

Emperror: A Flexible Generative Perception Error Model for Probing Self-Driving Planners

RA-L 2025

To handle the complexities of real-world traffic, learning planners for self-driving from data is a promising direction. While recent approaches have shown great progress, they typically assume a setting in which the ground-truth world state is available as input. However, when deployed, planning ne

Cited by 2SourceScholar
2024

DualAD: Disentangling the Dynamic and Static World for End-to-End Driving

CVPR 2024poster

State-of-the-art approaches for autonomous driving integrate multiple sub-tasks of the overall driving task into a single pipeline that can be trained in an end-to-end fashion by passing latent representations between the different modules. In contrast to previous approaches that rely on a unified g…

Cited by 5SourcePDFScholar
2024

S.T.A.R.-Track: Latent Motion Models for End-to-End 3D Object Tracking With Adaptive Spatio-Temporal Appearance Representations

RA-L 2024

Following the tracking-by-attention paradigm, this letter introduces an object-centric, transformer-based framework for tracking in 3D. Traditional model-based tracking approaches incorporate the geometric effect of object- and ego motion between frames with a geometric motion model. Inspired by thi

Cited by 13SourceScholar
2023

PowerBEV: A Powerful Yet Lightweight Framework for Instance Prediction in Bird’s-Eye View

IJCAI 2023poster

Accurately perceiving instances and predicting their future motion are key tasks for autonomous vehicles, enabling them to navigate safely in complex urban traffic. While bird’s-eye view (BEV) representations are commonplace in perception for autonomous driving, their potential in a motion predictio…

2022

KING: Generating Safety-Critical Driving Scenarios for Robust Imitation via Kinematics Gradients

ECCV 2022poster

"Simulators offer the possibility of safe, low-cost development of self-driving systems. However, current driving simulators exhibit naïve behavior models for background traffic. Hand-tuned scenarios are typically added during simulation to induce safety-critical situations. An alternative approach…