1 code implementation • Open Journal on ITS 2023 • Christopher Plachetka, Benjamin Sertolli, Jenny Fricke, Marvin Klingner, Tim Fingscheidt
In this work we present a novel deep learning-based approach to detect and specify map deviations in erroneous or outdated high-definition (HD) maps using both sensor and map data as input to a deep neural network (DNN).
no code implementations • 6 Jun 2023 • Shubhankar Borse, Senthil Yogamani, Marvin Klingner, Varun Ravi, Hong Cai, Abdulaziz Almuzairee, Fatih Porikli
Bird's-eye-view (BEV) grid is a typical representation of the perception of road components, e. g., drivable area, in autonomous driving.
no code implementations • 3 Mar 2023 • Marvin Klingner, Shubhankar Borse, Varun Ravi Kumar, Behnaz Rezaei, Venkatraman Narayanan, Senthil Yogamani, Fatih Porikli
Specifically, we propose cross-task distillation from an instance segmentation teacher (X-IS) in the PV feature extraction stage providing supervision without ambiguous error backpropagation through the view transformation.
no code implementations • CVPR 2023 • Marvin Klingner, Shubhankar Borse, Varun Ravi Kumar, Behnaz Rezaei, Venkatraman Narayanan, Senthil Yogamani, Fatih Porikli
Specifically, we propose cross-task distillation from an instance segmentation teacher (X-IS) in the PV feature extraction stage providing supervision without ambiguous error backpropagation through the view transformation.
Ranked #5 on 3D Object Detection on nuscenes Camera-Radar
no code implementations • 13 Oct 2022 • Shubhankar Borse, Marvin Klingner, Varun Ravi Kumar, Hong Cai, Abdulaziz Almuzairee, Senthil Yogamani, Fatih Porikli
Bird's-eye-view (BEV) grid is a common representation for the perception of road components, e. g., drivable area, in autonomous driving.
no code implementations • 1 Jun 2022 • Marvin Klingner, Konstantin Müller, Mona Mirzaie, Jasmin Breitenstein, Jan-Aike Termöhlen, Tim Fingscheidt
The emergence of data-driven machine learning (ML) has facilitated significant progress in many complicated tasks such as highly-automated driving.
1 code implementation • 2 Mar 2022 • Marvin Klingner, Varun Ravi Kumar, Senthil Yogamani, Andreas Bär, Tim Fingscheidt
In this paper, we (i) propose a novel adversarial perturbation detection scheme based on multi-task perception of complex vision tasks (i. e., depth estimation and semantic segmentation).
1 code implementation • 2 Mar 2022 • Marvin Klingner, Mouadh Ayache, Tim Fingscheidt
In this work, we further expand a source-free UDA approach to a continual and therefore online-capable UDA on a single-image basis for semantic segmentation.
no code implementations • 29 Apr 2021 • Sebastian Houben, Stephanie Abrecht, Maram Akila, Andreas Bär, Felix Brockherde, Patrick Feifel, Tim Fingscheidt, Sujan Sai Gannamaneni, Seyed Eghbal Ghobadi, Ahmed Hammam, Anselm Haselhoff, Felix Hauser, Christian Heinzemann, Marco Hoffmann, Nikhil Kapoor, Falk Kappel, Marvin Klingner, Jan Kronenberger, Fabian Küppers, Jonas Löhdefink, Michael Mlynarski, Michael Mock, Firas Mualla, Svetlana Pavlitskaya, Maximilian Poretschkin, Alexander Pohl, Varun Ravi-Kumar, Julia Rosenzweig, Matthias Rottmann, Stefan Rüping, Timo Sämann, Jan David Schneider, Elena Schulz, Gesina Schwalbe, Joachim Sicking, Toshika Srivastava, Serin Varghese, Michael Weber, Sebastian Wirkert, Tim Wirtz, Matthias Woehrle
Our paper addresses both machine learning experts and safety engineers: The former ones might profit from the broad range of machine learning topics covered and discussions on limitations of recent methods.
no code implementations • 12 Apr 2021 • Marvin Klingner, Andreas Bär, Marcel Mross, Tim Fingscheidt
In this work we address the task of observing the performance of a semantic segmentation deep neural network (DNN) during online operation, i. e., during inference, which is of high importance in safety-critical applications such as autonomous driving.
no code implementations • 9 Apr 2021 • Varun Ravi Kumar, Marvin Klingner, Senthil Yogamani, Markus Bach, Stefan Milz, Tim Fingscheidt, Patrick Mäder
We evaluate our approach on the Fisheye WoodScape surround-view dataset, significantly improving over previous approaches.
2 code implementations • 17 Nov 2020 • Marvin Klingner, Jan-Aike Termöhlen, Jacob Ritterbach, Tim Fingscheidt
In this paper we present a solution to the task of "unsupervised domain adaptation (UDA) of a given pre-trained semantic segmentation model without relying on any source domain representations".
no code implementations • 10 Aug 2020 • Varun Ravi Kumar, Marvin Klingner, Senthil Yogamani, Stefan Milz, Tim Fingscheidt, Patrick Maeder
This paper introduces a novel multi-task learning strategy to improve self-supervised monocular distance estimation on fisheye and pinhole camera images.
1 code implementation • ECCV 2020 • Marvin Klingner, Jan-Aike Termöhlen, Jonas Mikolajczyk, Tim Fingscheidt
Self-supervised monocular depth estimation presents a powerful method to obtain 3D scene information from single camera images, which is trainable on arbitrary image sequences without requiring depth labels, e. g., from a LiDAR sensor.
no code implementations • 15 Jun 2020 • Jonas Löhdefink, Justin Fehrling, Marvin Klingner, Fabian Hüger, Peter Schlicht, Nico M. Schmidt, Tim Fingscheidt
Autonomous driving requires self awareness of its perception functions.
1 code implementation • 12 May 2020 • Marvin Klingner, Andreas Bär, Philipp Donn, Tim Fingscheidt
While neural networks trained for semantic segmentation are essential for perception in autonomous driving, most current algorithms assume a fixed number of classes, presenting a major limitation when developing new autonomous driving systems with the need of additional classes.
no code implementations • 23 Apr 2020 • Marvin Klingner, Andreas Bär, Tim Fingscheidt
We show the effectiveness of our method on the Cityscapes dataset, where our multi-task training approach consistently outperforms the single-task semantic segmentation baseline in terms of both robustness vs. noise and in terms of adversarial attacks, without the need for depth labels in training.