1 code implementation • 27 Jun 2023 • Jannik Zürn, Ingmar Posner, Wolfram Burgard
To overcome this limitation, we propose to use the motion patterns of traffic participants as lane graph annotations.
no code implementations • CVPR 2023 • Martin Büchner, Jannik Zürn, Ion-George Todoran, Abhinav Valada, Wolfram Burgard
To overcome these challenges, we propose a novel bottom-up approach to lane graph estimation from aerial imagery that aggregates multiple overlapping graphs into a single consistent graph.
no code implementations • 12 Sep 2022 • Jannik Zürn, Sebastian Weber, Wolfram Burgard
Robustly classifying ground infrastructure such as roads and street crossings is an essential task for mobile robots operating alongside pedestrians.
no code implementations • 30 Jan 2022 • Jannik Zürn, Wolfram Burgard
In extensive experiments carried out with a real-world dataset, we demonstrate that our approach provides accurate detections of moving vehicles and does not require manual annotations.
1 code implementation • 1 May 2021 • Jannik Zürn, Johan Vertens, Wolfram Burgard
Lane-level scene annotations provide invaluable data in autonomous vehicles for trajectory planning in complex environments such as urban areas and cities.
Ranked #2 on Lane Detection on nuScenes
no code implementations • 10 Mar 2020 • Johan Vertens, Jannik Zürn, Wolfram Burgard
We avoid the expensive annotation of nighttime images by leveraging an existing daytime RGB-dataset and propose a teacher-student training approach that transfers the dataset's knowledge to the nighttime domain.
no code implementations • 6 Dec 2019 • Jannik Zürn, Wolfram Burgard, Abhinav Valada
In this work, we propose a novel terrain classification framework leveraging an unsupervised proprioceptive classifier that learns from vehicle-terrain interaction sounds to self-supervise an exteroceptive classifier for pixel-wise semantic segmentation of images.