@article{HaslerHuberLeindeckeretal.2013, author = {Hasler, Ewald and Huber, Christian and Leindecker, Herbert C. and Schrag, Tobias and Stocker, Emanuel and Wartha, Christian}, title = {Bewertung unterschiedlicher Fassadenkonstruktionen unter Lebenszyklusaspekten mit einer Fallstudie B{\"u}rogeb{\"a}ude}, volume = {35}, journal = {Bauphysik}, number = {4}, publisher = {Ernst \& Sohn}, address = {Berlin}, issn = {1437-0980}, doi = {https://doi.org/10.1002/bapi.201310072}, pages = {250 -- 256}, year = {2013}, language = {de} } @unpublished{NovickiNetoRewayPolednaetal.2023, author = {Novicki Neto, Leonardo and Reway, Fabio and Poledna, Yuri and Funk Drechsler, Maikol and Parente Ribeiro, Eduardo and Huber, Werner and Icking, Christian}, title = {TWICE Dataset: Digital Twin of Test Scenarios in a Controlled Environment}, publisher = {arXiv}, address = {Ithaca}, doi = {https://doi.org/10.48550/arXiv.2310.03895}, year = {2023}, abstract = {Ensuring the safe and reliable operation of autonomous vehicles under adverse weather remains a significant challenge. To address this, we have developed a comprehensive dataset composed of sensor data acquired in a real test track and reproduced in the laboratory for the same test scenarios. The provided dataset includes camera, radar, LiDAR, inertial measurement unit (IMU), and GPS data recorded under adverse weather conditions (rainy, night-time, and snowy conditions). We recorded test scenarios using objects of interest such as car, cyclist, truck and pedestrian -- some of which are inspired by EURONCAP (European New Car Assessment Programme). The sensor data generated in the laboratory is acquired by the execution of simulation-based tests in hardware-in-the-loop environment with the digital twin of each real test scenario. The dataset contains more than 2 hours of recording, which totals more than 280GB of data. Therefore, it is a valuable resource for researchers in the field of autonomous vehicles to test and improve their algorithms in adverse weather conditions, as well as explore the simulation-to-reality gap.}, language = {en} } @unpublished{FidelisRewayRibeiroetal.2023, author = {Fidelis, Eduardo and Reway, Fabio and Ribeiro, Herick Y. S. and Campos, Pietro and Huber, Werner and Icking, Christian and Faria, Lester and Sch{\"o}n, Torsten}, title = {Generation of Realistic Synthetic Raw Radar Data for Automated Driving Applications using Generative Adversarial Networks}, publisher = {arXiv}, address = {Ithaca}, doi = {https://doi.org/10.48550/arXiv.2308.02632}, year = {2023}, abstract = {The main approaches for simulating FMCW radar are based on ray tracing, which is usually computationally intensive and do not account for background noise. This work proposes a faster method for FMCW radar simulation capable of generating synthetic raw radar data using generative adversarial networks (GAN). The code and pre-trained weights are open-source and available on GitHub. This method generates 16 simultaneous chirps, which allows the generated data to be used for the further development of algorithms for processing radar data (filtering and clustering). This can increase the potential for data augmentation, e.g., by generating data in non-existent or safety-critical scenarios that are not reproducible in real life. In this work, the GAN was trained with radar measurements of a motorcycle and used to generate synthetic raw radar data of a motorcycle traveling in a straight line. For generating this data, the distance of the motorcycle and Gaussian noise are used as input to the neural network. The synthetic generated radar chirps were evaluated using the Frechet Inception Distance (FID). Then, the Range-Azimuth (RA) map is calculated twice: first, based on synthetic data using this GAN and, second, based on real data. Based on these RA maps, an algorithm with adaptive threshold and edge detection is used for object detection. The results have shown that the data is realistic in terms of coherent radar reflections of the motorcycle and background noise based on the comparison of chirps, the RA maps and the object detection results. Thus, the proposed method in this work has shown to minimize the simulation-to-reality gap for the generation of radar data.}, language = {en} } @inproceedings{PolednaRewayFunkDrechsleretal.2023, author = {Poledna, Yuri and Reway, Fabio and Funk Drechsler, Maikol and Huber, Werner and Icking, Christian and Parente Ribeiro, Eduardo}, title = {An Open-Source High-Level Fusion Algorithm in ROS for Automated Driving Applications}, booktitle = {Proceedings: 2022 10th International Conference in Software Engineering Research and Innovation (CONISOFT 2022)}, editor = {Ju{\´a}rez-Ram{\´i}rez, Reyes and Fern{\´a}ndez y Fern{\´a}ndez, Carlos and Perez Gonzalez, Hector G. and Ram{\´i}rez-Noriega, Alan and Jim{\´e}nez, Samantha and Guerra-Garc{\´i}a, C{\´e}sar and Licea Sandoval, Guillermo}, publisher = {IEEE}, address = {Los Alamitos}, isbn = {978-1-6654-6126-9}, doi = {https://doi.org/10.1109/CONISOFT55708.2022.00031}, pages = {174 -- 181}, year = {2023}, language = {en} } @inproceedings{WachtelGranadoSchuesslervondemBusscheHuennefeldetal.2024, author = {Wachtel Granado, Diogo and Sch{\"u}ßler, Christian and von dem Bussche-H{\"u}nnefeld, Tetmar and Rothmeier, Thomas and Vossiek, Martin and Huber, Werner}, title = {Evaluation of Automotive Radar Simulation Tools for Adverse Weather Conditions: A Comparative Analysis of Real Measurement, HFSS and Monte-Carlo GO Methods}, booktitle = {2024 IEEE Radar Conference (RadarConf24) Proceedings}, publisher = {IEEE}, address = {Piscataway}, isbn = {979-8-3503-2920-9}, doi = {https://doi.org/10.1109/RadarConf2458775.2024.10549398}, year = {2024}, language = {en} } @article{GeibNeblHuberetal.2020, author = {Geib, Christian and Nebl, Christoph and Huber, Josef and Schweiger, Hans-Georg}, title = {Herausforderung Elektrofahrzeuge}, volume = {2020}, journal = {Brandwacht: Zeitschrift f{\"u}r Brand- und Katastrophenschutz}, subtitle = {Hilfestellung f{\"u}r den Einsatz}, number = {1}, publisher = {Bayerisches Staatsministerium des Innern, f{\"u}r Sport und Integration}, address = {M{\"u}nchen}, url = {https://www.brandwacht.bayern.de/mam/archiv/beitraege_pdf/bw_1_2020_s18_20_e-autos2.pdf}, pages = {18 -- 20}, year = {2020}, language = {de} } @article{KueppersPerauFrankenetal.2020, author = {K{\"u}ppers, Martin and Perau, Christian and Franken, Marco and Heger, Hans J{\"o}rg and Huber, Matthias and Metzger, Michael and Niessen, Stefan}, title = {Data-Driven Regionalization of Decarbonized Energy Systems for Reflecting Their Changing Topologies in Planning and Optimization}, volume = {13}, pages = {4076}, journal = {Energies}, number = {16}, publisher = {MDPI}, address = {Basel}, issn = {1996-1073}, doi = {https://doi.org/10.3390/en13164076}, year = {2020}, abstract = {The decarbonization of energy systems has led to a fundamental change in their topology since generation is shifted to locations with favorable renewable conditions. In planning, this change is reflected by applying optimization models to regions within a country to optimize the distribution of generation units and to evaluate the resulting impact on the grid topology. This paper proposes a globally applicable framework to find a suitable regionalization for energy system models with a data-driven approach. Based on a global, spatially resolved database of demand, generation, and renewable profiles, hierarchical clustering with fine-tuning is performed. This regionalization approach is applied by modeling the resulting regions in an optimization model including a synthesized grid. In an exemplary case study, South Africa's energy system is examined. The results show that the data-driven regionalization is beneficial compared to the common approach of using political regions. Furthermore, the results of a modeled 80\% decarbonization until 2045 demonstrate that the integration of renewable energy sources fundamentally changes the role of regions within South Africa's energy system. Thereby, the electricity exchange between regions is also impacted, leading to a different grid topology. Using clustered regions improves the understanding and analysis of regional transformations in the decarbonization process.}, language = {en} } @inproceedings{RewayFunkDrechslerMurthyetal.2022, author = {Reway, Fabio and Funk Drechsler, Maikol and Murthy, Ravikiran and Poledna, Yuri and Huber, Werner and Icking, Christian}, title = {Simulation-based test methods with an automotive camera-in-the-loop for automated driving algorithms}, booktitle = {2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-6654-7095-7}, doi = {https://doi.org/10.1109/ICECCME55909.2022.9988437}, year = {2022}, language = {en} } @inproceedings{BotschHuberBalasubramanianetal.2023, author = {Botsch, Michael and Huber, Werner and Balasubramanian, Lakshman and Flores Fernandez, Alberto and Geisler, Markus and Gudera, Christian and Morales Gomez, Mauricio Rene and Riegl, Peter and S{\´a}nchez Morales, Eduardo and Chandra Sekaran, Karthikeyan and Weinzierl, Michael}, title = {Data Collection and Safety Use Cases in Smart Infrastructures}, booktitle = {15th International ACM Conference on Automotive User Interfaces: Adjunct Conference Proceedings}, publisher = {ACM}, address = {New York}, isbn = {979-8-4007-0112-2}, doi = {https://doi.org/10.1145/3581961.3609858}, pages = {333 -- 336}, year = {2023}, language = {en} } @inproceedings{HuberHamacherZiemsetal.2013, author = {Huber, Matthias and Hamacher, Thomas and Ziems, Christian and Weber, Harald}, title = {Combining LP and MIP approaches to model the impacts of renewable energy generation on individual thermal power plant operation}, booktitle = {2013 IEEE Power \& Energy Society General Meeting}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-4799-1303-9}, doi = {https://doi.org/10.1109/PESMG.2013.6672804}, year = {2013}, language = {en} } @article{NovickiNetoRewayPolednaetal.2025, author = {Novicki Neto, Leonardo and Reway, Fabio and Poledna, Yuri and Funk Drechsler, Maikol and Icking, Christian and Huber, Werner and Parente Ribeiro, Eduardo}, title = {TWICE dataset: digital twin of test scenarios in a controlled environment}, volume = {19}, journal = {International Journal of Vehicle Systems Modelling and Testing (IJVSMT)}, number = {2}, publisher = {Inderscience}, address = {Genf}, issn = {1745-6436}, doi = {https://doi.org/10.1504/IJVSMT.2025.147353}, pages = {152 -- 170}, year = {2025}, language = {en} }