M. Eng. Florian Heidecker

AI for Motion (AIM)

Telephone: +49 561 804-6018
Fax: +49 561 804-6022
Email: florian.heidecker[at]uni-kassel[dot]de

Site: Wilhelmshöher Allee 73
34121 Kassel

Room: WA-altes Gebäude (WA 73), ohne Raumangabe

Publications

[ 2023 ]
[ 2022 ]
[ 2021 ]
[ 2020 ]
[ 2019 ]

2023[ to top ]

Heidecker, F., El-Khateeb, A., Sick, B.: Sampling-based Uncertainty Estimation for an Instance Segmentation Network arXiv e-prints. arXiv:2305.14977 (2023).
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The examination of uncertainty in the predictions of machine learning (ML) models is receiving increasing attention. One uncertainty modeling technique used for this purpose is Monte-Carlo (MC)-Dropout, where repeated predictions are generated for a single input. Therefore, clustering is required to describe the resulting uncertainty, but only through efficient clustering is it possible to describe the uncertainty from the model attached to each object. This article uses Bayesian Gaussian Mixture (BGM) to solve this problem. In addition, we investigate different values for the dropout rate and other techniques, such as focal loss and calibration, which we integrate into the Mask-RCNN model to obtain the most accurate uncertainty approximation of each instance and showcase it graphically.
@article{heidecker2023sampling, abstract = {The examination of uncertainty in the predictions of machine learning (ML) models is receiving increasing attention. One uncertainty modeling technique used for this purpose is Monte-Carlo (MC)-Dropout, where repeated predictions are generated for a single input. Therefore, clustering is required to describe the resulting uncertainty, but only through efficient clustering is it possible to describe the uncertainty from the model attached to each object. This article uses Bayesian Gaussian Mixture (BGM) to solve this problem. In addition, we investigate different values for the dropout rate and other techniques, such as focal loss and calibration, which we integrate into the Mask-RCNN model to obtain the most accurate uncertainty approximation of each instance and showcase it graphically.}, author = {Heidecker, Florian and El-Khateeb, Ahmad and Sick, Bernhard}, journal = {arXiv e-prints}, keywords = {itegpub}, pages = {arXiv:2305.14977}, title = {Sampling-based Uncertainty Estimation for an Instance Segmentation Network}, year = 2023 }
%0 Journal Article %1 heidecker2023sampling %A Heidecker, Florian %A El-Khateeb, Ahmad %A Sick, Bernhard %D 2023 %J arXiv e-prints %P arXiv:2305.14977 %T Sampling-based Uncertainty Estimation for an Instance Segmentation Network %U https://arxiv.org/abs/2305.14977 %X The examination of uncertainty in the predictions of machine learning (ML) models is receiving increasing attention. One uncertainty modeling technique used for this purpose is Monte-Carlo (MC)-Dropout, where repeated predictions are generated for a single input. Therefore, clustering is required to describe the resulting uncertainty, but only through efficient clustering is it possible to describe the uncertainty from the model attached to each object. This article uses Bayesian Gaussian Mixture (BGM) to solve this problem. In addition, we investigate different values for the dropout rate and other techniques, such as focal loss and calibration, which we integrate into the Mask-RCNN model to obtain the most accurate uncertainty approximation of each instance and showcase it graphically.

2022[ to top ]

Rösch, K., Heidecker, F., Truetsch, J., Kowol, K., Schicktanz, C., Bieshaar, M., Sick, B., Stiller, C.: Space, Time, and Interaction: A Taxonomy of Corner Cases in Trajectory Datasets for Automated Driving In: IEEE Symposium on Computational Intelligence in Vehicles and Transportation Systems (IEEE CIVTS), IEEE SSCI. bll 86–93. IEEE (2022). https://doi.org/10.1109/SSCI51031.2022.10022241.
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Trajectory data analysis is an essential component for highly automated driving. Complex models developed with these data predict other road users’ movement and behavior patterns. Based on these predictions — and additional contextual information such as the course of the road, (traffic) rules, and interaction with other road users — the highly automated vehicle (HAV) must be able to reliably and safely perform the task assigned to it, e.g., moving from point A to B. Ideally, the HAV moves safely through its environment, just as we would expect a human driver to do. However, if unusual trajectories occur, so-called trajectory corner cases, a human driver can usually cope well, but an HAV can quickly get into trouble. In the definition of trajectory corner cases, which we provide in this work, we will consider the relevance of unusual trajectories with respect to the task at hand. Based on this, we will also present a taxonomy of different trajectory corner cases. The categorization of corner cases into the taxonomy will be shown with examples and is done by cause and required data sources. To illustrate the complexity between the machine learning (ML) model and the corner case cause, we present a general processing chain underlying the taxonomy.
@inproceedings{rosch2022space, abstract = {Trajectory data analysis is an essential component for highly automated driving. Complex models developed with these data predict other road users’ movement and behavior patterns. Based on these predictions — and additional contextual information such as the course of the road, (traffic) rules, and interaction with other road users — the highly automated vehicle (HAV) must be able to reliably and safely perform the task assigned to it, e.g., moving from point A to B. Ideally, the HAV moves safely through its environment, just as we would expect a human driver to do. However, if unusual trajectories occur, so-called trajectory corner cases, a human driver can usually cope well, but an HAV can quickly get into trouble. In the definition of trajectory corner cases, which we provide in this work, we will consider the relevance of unusual trajectories with respect to the task at hand. Based on this, we will also present a taxonomy of different trajectory corner cases. The categorization of corner cases into the taxonomy will be shown with examples and is done by cause and required data sources. To illustrate the complexity between the machine learning (ML) model and the corner case cause, we present a general processing chain underlying the taxonomy.}, author = {Rösch, Kevin and Heidecker, Florian and Truetsch, Julian and Kowol, Kamil and Schicktanz, Clemens and Bieshaar, Maarten and Sick, Bernhard and Stiller, Christoph}, booktitle = {IEEE Symposium on Computational Intelligence in Vehicles and Transportation Systems (IEEE CIVTS), IEEE SSCI}, keywords = {itegpub}, pages = {86–93}, publisher = {IEEE}, title = {Space, Time, and Interaction: A Taxonomy of Corner Cases in Trajectory Datasets for Automated Driving}, year = 2022 }
%0 Conference Paper %1 rosch2022space %A Rösch, Kevin %A Heidecker, Florian %A Truetsch, Julian %A Kowol, Kamil %A Schicktanz, Clemens %A Bieshaar, Maarten %A Sick, Bernhard %A Stiller, Christoph %B IEEE Symposium on Computational Intelligence in Vehicles and Transportation Systems (IEEE CIVTS), IEEE SSCI %D 2022 %I IEEE %P 86--93 %R 10.1109/SSCI51031.2022.10022241 %T Space, Time, and Interaction: A Taxonomy of Corner Cases in Trajectory Datasets for Automated Driving %U https://ieeexplore.ieee.org/document/10022241 %X Trajectory data analysis is an essential component for highly automated driving. Complex models developed with these data predict other road users’ movement and behavior patterns. Based on these predictions — and additional contextual information such as the course of the road, (traffic) rules, and interaction with other road users — the highly automated vehicle (HAV) must be able to reliably and safely perform the task assigned to it, e.g., moving from point A to B. Ideally, the HAV moves safely through its environment, just as we would expect a human driver to do. However, if unusual trajectories occur, so-called trajectory corner cases, a human driver can usually cope well, but an HAV can quickly get into trouble. In the definition of trajectory corner cases, which we provide in this work, we will consider the relevance of unusual trajectories with respect to the task at hand. Based on this, we will also present a taxonomy of different trajectory corner cases. The categorization of corner cases into the taxonomy will be shown with examples and is done by cause and required data sources. To illustrate the complexity between the machine learning (ML) model and the corner case cause, we present a general processing chain underlying the taxonomy.
Heidecker, F.: Detecting Corner Case in the Context of Highly Automated Driving In: Tomforde, S. en Krupitzer, C. (reds) Organic Computing -- Doctoral Dissertation Colloquium 2021. bll 60–73. kassel university press (2022).
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This article introduces a research proposal that aims to deﬁne, detect, and evaluate corner cases in the context of machine learning (ML) systems and highly automated driving. Corner cases are scenarios or data in a dataset that are recorded only very rarely and therefore only available in small quantities or not at all in the dataset. However, corner cases are very important for model training, as they are needed to improve the model performance in these rare scenarios. On the other hand, in safety- and security-critical applications, such as highly automated driving, corner case data is extremely important for testing and verifying the ML system. The research proposal addresses these challenges and focuses on the deﬁnition and categorization of corner cases. Based on this, ML methods for corner case detection are developed, focusing on object detection for automated driving and trajectory data for intention detection of pedestrians and cyclists. Finally, the research proposal addresses corner case metrics and the evaluation of the developed detection models.
@incollection{florianheidecker2022detecting, abstract = {This article introduces a research proposal that aims to deﬁne, detect, and evaluate corner cases in the context of machine learning (ML) systems and highly automated driving. Corner cases are scenarios or data in a dataset that are recorded only very rarely and therefore only available in small quantities or not at all in the dataset. However, corner cases are very important for model training, as they are needed to improve the model performance in these rare scenarios. On the other hand, in safety- and security-critical applications, such as highly automated driving, corner case data is extremely important for testing and verifying the ML system. The research proposal addresses these challenges and focuses on the deﬁnition and categorization of corner cases. Based on this, ML methods for corner case detection are developed, focusing on object detection for automated driving and trajectory data for intention detection of pedestrians and cyclists. Finally, the research proposal addresses corner case metrics and the evaluation of the developed detection models.}, author = {Heidecker, Florian}, booktitle = {Organic Computing – Doctoral Dissertation Colloquium 2021}, editor = {Tomforde, Sven and Krupitzer, Christian}, keywords = {itegpub}, pages = {60–73}, publisher = {kassel university press}, title = {Detecting Corner Case in the Context of Highly Automated Driving}, year = 2022 }
%0 Book Section %1 florianheidecker2022detecting %A Heidecker, Florian %B Organic Computing -- Doctoral Dissertation Colloquium 2021 %D 2022 %E Tomforde, Sven %E Krupitzer, Christian %I kassel university press %P 60--73 %T Detecting Corner Case in the Context of Highly Automated Driving %X This article introduces a research proposal that aims to deﬁne, detect, and evaluate corner cases in the context of machine learning (ML) systems and highly automated driving. Corner cases are scenarios or data in a dataset that are recorded only very rarely and therefore only available in small quantities or not at all in the dataset. However, corner cases are very important for model training, as they are needed to improve the model performance in these rare scenarios. On the other hand, in safety- and security-critical applications, such as highly automated driving, corner case data is extremely important for testing and verifying the ML system. The research proposal addresses these challenges and focuses on the deﬁnition and categorization of corner cases. Based on this, ML methods for corner case detection are developed, focusing on object detection for automated driving and trajectory data for intention detection of pedestrians and cyclists. Finally, the research proposal addresses corner case metrics and the evaluation of the developed detection models.

2021[ to top ]

Heidecker, F., Hannan, A., Bieshaar, M., Sick, B.: Towards Corner Case Detection by Modeling the Uncertainty of Instance Segmentation Networks In: Workshop on Integrated Artificial Intelligence in Data Science, ICPR. bll 361–374. IEEE, Milan, Italy (2021). https://doi.org/10.1007/978-3-030-68799-1_26.
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@inproceedings{heidecker2021towards, address = {Milan, Italy}, author = {Heidecker, Florian and Hannan, Abdul and Bieshaar, Maarten and Sick, Bernhard}, booktitle = {Workshop on Integrated Artificial Intelligence in Data Science, ICPR}, keywords = {itegpub}, pages = {361–374}, publisher = {IEEE}, title = {Towards Corner Case Detection by Modeling the Uncertainty of Instance Segmentation Networks}, year = 2021 }
%0 Conference Paper %1 heidecker2021towards %A Heidecker, Florian %A Hannan, Abdul %A Bieshaar, Maarten %A Sick, Bernhard %B Workshop on Integrated Artificial Intelligence in Data Science, ICPR %C Milan, Italy %D 2021 %I IEEE %P 361--374 %R 10.1007/978-3-030-68799-1_26 %T Towards Corner Case Detection by Modeling the Uncertainty of Instance Segmentation Networks
Möller, F., Botache, D., Huseljic, D., Heidecker, F., Bieshaar, M., Sick, B.: Out-of-distribution Detection and Generation using Soft Brownian Offset Sampling and Autoencoders In: Workshop on Safe Artificial Intelligence for Automated Driving (SAIAD), CVPR. bll 1–10 (2021).
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@inproceedings{moeller2021out, author = {Möller, Felix and Botache, Diego and Huseljic, Denis and Heidecker, Florian and Bieshaar, Maarten and Sick, Bernhard}, booktitle = {Workshop on Safe Artificial Intelligence for Automated Driving (SAIAD), CVPR}, keywords = {itegpub}, pages = {1–10}, title = {{Out-of-distribution Detection and Generation using Soft Brownian Offset Sampling and Autoencoders}}, year = 2021 }
%0 Conference Paper %1 moeller2021out %A Möller, Felix %A Botache, Diego %A Huseljic, Denis %A Heidecker, Florian %A Bieshaar, Maarten %A Sick, Bernhard %B Workshop on Safe Artificial Intelligence for Automated Driving (SAIAD), CVPR %D 2021 %P 1--10 %T Out-of-distribution Detection and Generation using Soft Brownian Offset Sampling and Autoencoders %U https://openaccess.thecvf.com/content/CVPR2021W/SAIAD/papers/Moller_Out-of-Distribution_Detection_and_Generation_Using_Soft_Brownian_Offset_Sampling_and_CVPRW_2021_paper.pdf
Heidecker, F., Gruhl, C., Sick, B.: Novelty based Driver Identification on RR Intervals from ECG Data In: Workshop on Integrated Artificial Intelligence in Data Science, ICPR. bll 407–421. IEEE, Milan, Italy (2021). https://doi.org/10.1007/978-3-030-68799-1_29.
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@inproceedings{heidecker2021novelty, address = {Milan, Italy}, author = {Heidecker, Florian and Gruhl, Christian and Sick, Bernhard}, booktitle = {Workshop on Integrated Artificial Intelligence in Data Science, ICPR}, keywords = {itegpub}, pages = {407–421}, publisher = {IEEE}, title = {Novelty based Driver Identification on RR Intervals from ECG Data}, year = 2021 }
%0 Conference Paper %1 heidecker2021novelty %A Heidecker, Florian %A Gruhl, Christian %A Sick, Bernhard %B Workshop on Integrated Artificial Intelligence in Data Science, ICPR %C Milan, Italy %D 2021 %I IEEE %P 407--421 %R 10.1007/978-3-030-68799-1_29 %T Novelty based Driver Identification on RR Intervals from ECG Data
Schneegans, J., Bieshaar, M., Heidecker, F., Sick, B.: Intelligent and Interactive Video Annotation for Instance Segmentation using Siamese Neural Networks In: Workshop on Integrated Artificial Intelligence in Data Science, ICPR. bll 375–389. IEEE, Milan, Italy (2021). https://doi.org/10.1007/978-3-030-68799-1_27.
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@inproceedings{schneegans2021intelligent, address = {Milan, Italy}, author = {Schneegans, Jan and Bieshaar, Maarten and Heidecker, Florian and Sick, Bernhard}, booktitle = {Workshop on Integrated Artificial Intelligence in Data Science, ICPR}, keywords = {itegpub}, pages = {375–389}, publisher = {IEEE}, title = {Intelligent and Interactive Video Annotation for Instance Segmentation using Siamese Neural Networks}, year = 2021 }
%0 Conference Paper %1 schneegans2021intelligent %A Schneegans, Jan %A Bieshaar, Maarten %A Heidecker, Florian %A Sick, Bernhard %B Workshop on Integrated Artificial Intelligence in Data Science, ICPR %C Milan, Italy %D 2021 %I IEEE %P 375--389 %R 10.1007/978-3-030-68799-1_27 %T Intelligent and Interactive Video Annotation for Instance Segmentation using Siamese Neural Networks
Bogdoll, D., Breitenstein, J., Heidecker, F., Bieshaar, M., Sick, B., Fingscheidt, T., Zöllner, J.M.: Description of Corner Cases in Automated Driving: Goals and Challenges In: Embedded and Real-World Computer Vision in Autonomous Driving (ERCVAD), ICCV. bll 1023–1028,. IEEE (2021). https://doi.org/10.1109/iccvw54120.2021.00119.
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@inproceedings{bogdoll2021description, author = {Bogdoll, Daniel and Breitenstein, Jasmin and Heidecker, Florian and Bieshaar, Maarten and Sick, Bernhard and Fingscheidt, Tim and Zöllner, J. Marius}, booktitle = {Embedded and Real-World Computer Vision in Autonomous Driving (ERCVAD), ICCV}, keywords = {itegpub}, pages = {1023–1028,}, publisher = {IEEE}, title = {Description of Corner Cases in Automated Driving: Goals and Challenges}, year = 2021 }
%0 Conference Paper %1 bogdoll2021description %A Bogdoll, Daniel %A Breitenstein, Jasmin %A Heidecker, Florian %A Bieshaar, Maarten %A Sick, Bernhard %A Fingscheidt, Tim %A Zöllner, J. Marius %B Embedded and Real-World Computer Vision in Autonomous Driving (ERCVAD), ICCV %D 2021 %I IEEE %P 1023--1028, %R 10.1109/iccvw54120.2021.00119 %T Description of Corner Cases in Automated Driving: Goals and Challenges
Heidecker, F., Breitenstein, J., Rösch, K., Löhdefink, J., Bieshaar, M., Stiller, C., Fingscheidt, T., Sick, B.: An Application-Driven Conceptualization of Corner Cases for Perception in Highly Automated Driving In: IEEE Intelligent Vehicles Symposium (IV). bll 644–651. IEEE, Nagoya, Japan (2021). https://doi.org/10.1109/iv48863.2021.9575933.
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@inproceedings{heidecker2021application, address = {Nagoya, Japan}, author = {Heidecker, Florian and Breitenstein, Jasmin and Rösch, Kevin and Löhdefink, Jonas and Bieshaar, Maarten and Stiller, Christoph and Fingscheidt, Tim and Sick, Bernhard}, booktitle = {IEEE Intelligent Vehicles Symposium (IV)}, keywords = {itegpub}, pages = {644–651}, publisher = {IEEE}, title = {{An Application-Driven Conceptualization of Corner Cases for Perception in Highly Automated Driving}}, year = 2021 }
%0 Conference Paper %1 heidecker2021application %A Heidecker, Florian %A Breitenstein, Jasmin %A Rösch, Kevin %A Löhdefink, Jonas %A Bieshaar, Maarten %A Stiller, Christoph %A Fingscheidt, Tim %A Sick, Bernhard %B IEEE Intelligent Vehicles Symposium (IV) %C Nagoya, Japan %D 2021 %I IEEE %P 644--651 %R 10.1109/iv48863.2021.9575933 %T An Application-Driven Conceptualization of Corner Cases for Perception in Highly Automated Driving

2020[ to top ]

Scharei, K., Heidecker, F., Bieshaar, M.: Knowledge Representations in Technical Systems -- A Taxonomy arXiv e-prints. arXiv:2001.04835 (2020).
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@article{scharei2020knowledge, author = {Scharei, Kristina and Heidecker, Florian and Bieshaar, Maarten}, journal = {arXiv e-prints}, keywords = {Robotics}, pages = {arXiv:2001.04835}, title = {{Knowledge Representations in Technical Systems – A Taxonomy}}, year = 2020 }
%0 Journal Article %1 scharei2020knowledge %A Scharei, Kristina %A Heidecker, Florian %A Bieshaar, Maarten %D 2020 %J arXiv e-prints %P arXiv:2001.04835 %T Knowledge Representations in Technical Systems -- A Taxonomy

2019[ to top ]

Heidecker, F., Bieshaar, M., Sick, B.: Towards Corner Case Identification in Cyclists’ Trajectories In: ACM Computer Science in Cars Symposium (CSCS). ACM (2019).
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@inproceedings{heidecker2019towards, author = {Heidecker, F. and Bieshaar, M. and Sick, B.}, booktitle = {ACM Computer Science in Cars Symposium (CSCS)}, keywords = {imported}, publisher = {ACM}, title = {Towards Corner Case Identification in Cyclists’ Trajectories}, year = 2019 }
%0 Conference Paper %1 heidecker2019towards %A Heidecker, F. %A Bieshaar, M. %A Sick, B. %B ACM Computer Science in Cars Symposium (CSCS) %D 2019 %I ACM %T Towards Corner Case Identification in Cyclists’ Trajectories %U https://cscs19.cispa.saarland/files/cscs19_camera_ready/28_fhmbbs_19_cscs_.pdf