TY - JOUR A1 - Hasler, Ewald A1 - Huber, Christian A1 - Leindecker, Herbert C. A1 - Schrag, Tobias A1 - Stocker, Emanuel A1 - Wartha, Christian T1 - Bewertung unterschiedlicher Fassadenkonstruktionen unter Lebenszyklusaspekten mit einer Fallstudie Bürogebäude JF - Bauphysik UR - https://doi.org/10.1002/bapi.201310072 Y1 - 2013 UR - https://doi.org/10.1002/bapi.201310072 SN - 1437-0980 VL - 35 IS - 4 SP - 250 EP - 256 PB - Ernst & Sohn CY - Berlin ER - TY - INPR A1 - Novicki Neto, Leonardo A1 - Reway, Fabio A1 - Poledna, Yuri A1 - Funk Drechsler, Maikol A1 - Parente Ribeiro, Eduardo A1 - Huber, Werner A1 - Icking, Christian T1 - TWICE Dataset: Digital Twin of Test Scenarios in a Controlled Environment N2 - 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. UR - https://doi.org/10.48550/arXiv.2310.03895 Y1 - 2023 UR - https://doi.org/10.48550/arXiv.2310.03895 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-59887 PB - arXiv CY - Ithaca ER - TY - INPR A1 - Fidelis, Eduardo A1 - Reway, Fabio A1 - Ribeiro, Herick Y. S. A1 - Campos, Pietro A1 - Huber, Werner A1 - Icking, Christian A1 - Faria, Lester A1 - Schön, Torsten T1 - Generation of Realistic Synthetic Raw Radar Data for Automated Driving Applications using Generative Adversarial Networks N2 - 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. UR - https://doi.org/10.48550/arXiv.2308.02632 Y1 - 2023 UR - https://doi.org/10.48550/arXiv.2308.02632 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-59872 PB - arXiv CY - Ithaca ER - TY - CHAP A1 - Poledna, Yuri A1 - Reway, Fabio A1 - Funk Drechsler, Maikol A1 - Huber, Werner A1 - Icking, Christian A1 - Parente Ribeiro, Eduardo ED - Juárez-Ramírez, Reyes ED - Fernández y Fernández, Carlos ED - Perez Gonzalez, Hector G. ED - Ramírez-Noriega, Alan ED - Jiménez, Samantha ED - Guerra-García, César ED - Licea Sandoval, Guillermo T1 - An Open-Source High-Level Fusion Algorithm in ROS for Automated Driving Applications T2 - Proceedings: 2022 10th International Conference in Software Engineering Research and Innovation (CONISOFT 2022) UR - https://doi.org/10.1109/CONISOFT55708.2022.00031 Y1 - 2023 UR - https://doi.org/10.1109/CONISOFT55708.2022.00031 SN - 978-1-6654-6126-9 SP - 174 EP - 181 PB - IEEE CY - Los Alamitos ER - TY - CHAP A1 - Wachtel Granado, Diogo A1 - Schüßler, Christian A1 - von dem Bussche-Hünnefeld, Tetmar A1 - Rothmeier, Thomas A1 - Vossiek, Martin A1 - Huber, Werner T1 - Evaluation of Automotive Radar Simulation Tools for Adverse Weather Conditions: A Comparative Analysis of Real Measurement, HFSS and Monte-Carlo GO Methods T2 - 2024 IEEE Radar Conference (RadarConf24) Proceedings UR - https://doi.org/10.1109/RadarConf2458775.2024.10549398 Y1 - 2024 UR - https://doi.org/10.1109/RadarConf2458775.2024.10549398 SN - 979-8-3503-2920-9 PB - IEEE CY - Piscataway ER - TY - JOUR A1 - Geib, Christian A1 - Nebl, Christoph A1 - Huber, Josef A1 - Schweiger, Hans-Georg T1 - Herausforderung Elektrofahrzeuge BT - Hilfestellung für den Einsatz JF - Brandwacht: Zeitschrift für Brand- und Katastrophenschutz Y1 - 2020 UR - https://www.brandwacht.bayern.de/mam/archiv/beitraege_pdf/bw_1_2020_s18_20_e-autos2.pdf VL - 2020 IS - 1 SP - 18 EP - 20 PB - Bayerisches Staatsministerium des Innern, für Sport und Integration CY - München ER - TY - JOUR A1 - Küppers, Martin A1 - Perau, Christian A1 - Franken, Marco A1 - Heger, Hans Jörg A1 - Huber, Matthias A1 - Metzger, Michael A1 - Niessen, Stefan T1 - Data-Driven Regionalization of Decarbonized Energy Systems for Reflecting Their Changing Topologies in Planning and Optimization JF - Energies N2 - 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. UR - https://doi.org/10.3390/en13164076 KW - spatial clustering KW - energy system model KW - optimization KW - GIS KW - South Africa KW - energy transition Y1 - 2020 UR - https://doi.org/10.3390/en13164076 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-31469 SN - 1996-1073 VL - 13 IS - 16 PB - MDPI CY - Basel ER - TY - CHAP A1 - Reway, Fabio A1 - Funk Drechsler, Maikol A1 - Murthy, Ravikiran A1 - Poledna, Yuri A1 - Huber, Werner A1 - Icking, Christian T1 - Simulation-based test methods with an automotive camera-in-the-loop for automated driving algorithms T2 - 2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME) UR - https://doi.org/10.1109/ICECCME55909.2022.9988437 KW - sensor-in-the-loop KW - automate driving KW - testing Y1 - 2022 UR - https://doi.org/10.1109/ICECCME55909.2022.9988437 SN - 978-1-6654-7095-7 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Botsch, Michael A1 - Huber, Werner A1 - Balasubramanian, Lakshman A1 - Flores Fernandez, Alberto A1 - Geisler, Markus A1 - Gudera, Christian A1 - Morales Gomez, Mauricio Rene A1 - Riegl, Peter A1 - Sánchez Morales, Eduardo A1 - Chandra Sekaran, Karthikeyan A1 - Weinzierl, Michael T1 - Data Collection and Safety Use Cases in Smart Infrastructures T2 - 15th International ACM Conference on Automotive User Interfaces: Adjunct Conference Proceedings UR - https://doi.org/10.1145/3581961.3609858 KW - smart infrastructure KW - infrared camera KW - LiDAR sensor KW - sensor data fusion KW - safety use case Y1 - 2023 UR - https://doi.org/10.1145/3581961.3609858 SN - 979-8-4007-0112-2 SP - 333 EP - 336 PB - ACM CY - New York ER - TY - CHAP A1 - Huber, Matthias A1 - Hamacher, Thomas A1 - Ziems, Christian A1 - Weber, Harald T1 - Combining LP and MIP approaches to model the impacts of renewable energy generation on individual thermal power plant operation T2 - 2013 IEEE Power & Energy Society General Meeting UR - https://doi.org/10.1109/PESMG.2013.6672804 Y1 - 2013 UR - https://doi.org/10.1109/PESMG.2013.6672804 SN - 978-1-4799-1303-9 PB - IEEE CY - Piscataway ER - TY - JOUR A1 - Novicki Neto, Leonardo A1 - Reway, Fabio A1 - Poledna, Yuri A1 - Funk Drechsler, Maikol A1 - Icking, Christian A1 - Huber, Werner A1 - Parente Ribeiro, Eduardo T1 - TWICE dataset: digital twin of test scenarios in a controlled environment JF - International Journal of Vehicle Systems Modelling and Testing (IJVSMT) UR - https://doi.org/10.1504/IJVSMT.2025.147353 Y1 - 2025 UR - https://doi.org/10.1504/IJVSMT.2025.147353 SN - 1745-6436 SN - 1745-6444 VL - 19 IS - 2 SP - 152 EP - 170 PB - Inderscience CY - Genf ER -