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AuthorTitleYearJournal/ProceedingsReftypeDOI/URL
Breitenstein, J., Heidecker, F., Lyssenko, M., Bogdoll, D., Bieshaar, M., Zöllner, J. M., Sick, B. & Fingscheidt, T. What Does Really Count? Estimating Relevance of Corner Cases for Semantic Segmentation in Automated Driving 2023 Workshop on roBustness and Reliability of Autonomous Vehicles in the Open-world (BRAVO), ICCV   inproceedings URL  
Abstract: In safety-critical applications such as automated driving, perception errors may create an imminent risk to vulnerable road users (VRU). To mitigate the occurrence of unexpected and potentially dangerous situations, so-called corner cases, perception models are trained on a huge amount of data. However, the models are typically evaluated using task-agnostic metrics, which do not reflect the severity of safety-critical misdetections. Consequently, misdetections with particular relevance for the safe driving task should entail a more severe penalty during evaluation to pinpoint corner cases in large-scale datasets. In this work, we propose a novel metric IoUw that exploits relevance on the pixel level of the semantic segmentation output to extend the notion of the intersection over union (IoU) by emphasizing small areas of an image affected by corner cases. We (i) employ IoUw to measure the effect of pre-defined relevance criteria on the segmentation evaluation, and (ii) use the relevance-adapted IoUw to refine the identification of corner cases. In our experiments, we investigate vision-based relevance criteria and physical attributes as per-pixel criticality to factor in the imminent risk, showing that IoUw precisely accentuates the relevance of corner cases.
BibTeX:
@inproceedings{breitenstein2023what,
  author = {Breitenstein, Jasmin and Heidecker, Florian and Lyssenko, Maria and Bogdoll, Daniel and Bieshaar, Maarten and Zöllner, J. Marius and Sick, Bernhard and Fingscheidt, Tim},
  title = {What Does Really Count? Estimating Relevance of Corner Cases for Semantic Segmentation in Automated Driving},
  booktitle = {Workshop on roBustness and Reliability of Autonomous Vehicles in the Open-world (BRAVO), ICCV},
  year = {2023},
  pages = {3991--4000},
  url = {https://openaccess.thecvf.com/content/ICCV2023W/BRAVO/html/Breitenstein_What_Does_Really_Count_Estimating_Relevance_of_Corner_Cases_for_ICCVW_2023_paper.html}
}
Huang, Z., He, Y. & Sick, B. Spatio-Temporal Attention Graph Neural Network for Remaining Useful Life Prediction 2023 Computational Science and Computational Intelligence (CSCI)   inproceedings  
Abstract: RUL prediction plays a crucial role in the health management of industrial systems. Given the increasing complexity of systems, data-driven predictive models have attracted significant research interest. Upon reviewing the existing literature, it appears that many studies either do not fully integrate both spatial and temporal features or employ only a single attention mechanism. Furthermore, there seems to be inconsistency in the choice of data normalization methods, particularly concerning operating conditions, which might influence predictive performance. To bridge these observations, this study presents the Spatio-Temporal Attention Graph Neural Network. Our model combines graph neural networks and temporal convolutional neural networks for spatial and temporal feature extraction, respectively. The cascade of these extractors, combined with multihead attention mechanisms for both spatio-temporal dimensions, aims to improve predictive precision and refine model explainability. Comprehensive experiments were conducted on the CMAPSS dataset to evaluate the impact of unified versus clustering normalization. The findings suggest that our model performs state-of-the-art results using only the unified normalization. Additionally, when dealing with datasets with multiple operating conditions, cluster normalization enhances the performance of our proposed model by up to 27%.
BibTeX:
@inproceedings{huang2023spatio,
  author = {Huang, Zhixin and He, Yujiang and Sick, Bernhard},
  title = {Spatio-Temporal Attention Graph Neural Network for Remaining Useful Life Prediction},
  booktitle = {Computational Science and Computational Intelligence (CSCI)},
  publisher = {IEEE},
  year = {2023},
  note = {(accepted)}
}
Lachi, V., Moallemy-Oureh, A., Roth, A. & Welke, P. Graph Pooling Provably Improves Expressivity 2023 Workshop on New Frontiers in Graph Learning, NeurIPS   inproceedings URL  
Abstract: In the domain of graph neural networks (GNNs), pooling operators are fundamental to reduce the size of the graph by simplifying graph structures and vertex features. Recent advances have shown that well-designed pooling operators, coupled with message-passing layers, can endow hierarchical GNNs with an expressive power regarding the graph isomorphism test that is equal to the Weisfeiler-Leman test. However, the ability of hierarchical GNNs to increase expressive power by utilizing graph coarsening was not yet explored. This results in uncertainties about the benefits of pooling operators and a lack of sufficient properties to guide their design. In this work, we identify conditions for pooling operators to generate WL-distinguishable coarsened graphs from originally WL-indistinguishable but non-isomorphic graphs. Our conditions are versatile and can be tailored to specific tasks and data characteristics, offering a promising avenue for further research.
BibTeX:
@inproceedings{lachi2023graph,
  author = {Lachi, Veronica and Moallemy-Oureh, Alice and Roth, Andreas and Welke, Pascal},
  title = {Graph Pooling Provably Improves Expressivity},
  booktitle = {Workshop on New Frontiers in Graph Learning, NeurIPS},
  year = {2023},
  url = {https://openreview.net/forum?id=lR5NYB9zrv}
}
Schreck, S., Reichert, H., Hetzel, M., Doll, K. & Sick, B. Height Change Feature Based Free Space Detection 2023 International Conference on Control, Mechatronics and Automation (ICCMA)   inproceedings DOI  
Abstract: We present a novel method for free space detection in dynamic environments like factory sites, crucial for autonomous forklifts to avoid collisions. It introduces a technique for fast surface normal estimation using spherical projected LiDAR data, which helps in detecting free space efficiently and in real-time. The method's effectiveness is proven with a 50.90% mIoU score on the Semantic KITTI dataset at 105 Hz, and a 63.30% mIoU score on a factory site dataset at 54 Hz.
BibTeX:
@inproceedings{schreck2023height,
  author = {Schreck, Steven and Reichert, Hannes and Hetzel, Manuel and Doll, Konrad and Sick, Bernhard},
  title = {Height Change Feature Based Free Space Detection},
  booktitle = {International Conference on Control, Mechatronics and Automation (ICCMA)},
  publisher = {IEEE},
  year = {2023},
  pages = {171--176},
  doi = {http://dx.doi.org/10.1109/ICCMA59762.2023.10374705}
}
Organic Computing -- Doctoral Dissertation Colloquium 2021 2022   book DOI  
BibTeX:
@book{tomforde2022organic,,
  title = {Organic Computing -- Doctoral Dissertation Colloquium 2021},
  publisher = {kassel university press},
  year = {2022},
  volume = {20},
  doi = {http://dx.doi.org/10.17170/kobra-202202215780}
}

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