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| Tekkaya, A. Erman |
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| Förster, Peter |
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| Mudimu, George T. |
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| Shibata, Lillian Marie |
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| Talabbeydokhti, Nasser |
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| Laffite, Ernesto Dante Rodriguez |
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| Schöpke, Benito |
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| Gobis, Anna |
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| Alfares, Hesham K. |
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| Münzel, Thomas |
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| Joy, Gemini Velleringatt |
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| Oubahman, Laila |
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| Filali, Youssef |
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| Philippi, Paula |
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| George, Alinda |
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| Lucia, Caterina De |
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| Avril, Ludovic |
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| Belachew, Zigyalew Gashaw |
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| Kassens-Noor, Eva | Darmstadt |
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| Cho, Seongchul |
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| Tonne, Cathryn |
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| Hosseinlou, Farhad |
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| Ganvit, Harsh |
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| Schmitt, Konrad Erich Kork |
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| Grimm, Daniel |
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Bäker, Bernard
in Cooperation with on an Cooperation-Score of 37%
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Publications (14/14 displayed)
- 2022Field Data Analysis of a Commercial Vehicle Fleet in Relation to the Load of the HV Battery
- 2021Active Output Selection for an HEV Boost Maneuver
- 2021Phenomenological, Measurement Based LiDAR Sensor Modelcitations
- 2021Simulation of Shield Current in Automotive High Voltage Systems
- 2021Data-Enhanced Battery Simulator for Testing Electric Powertrainscitations
- 2019Messelektronik in faser-elastomerverbünden zur drahtlosen und echtzeitfähigen messwerterfassung in motorradreifen
- 2019Energetisch optimal bemessene elektrische Maschinen für Mildhybridfahrzeuge
- 2017Optimal Velocity and Power Split Control of Hybrid Electric Vehicles
- 2016Analyse notwendiger Anforderungen an das Autonome Fahren im Automobilbereich und Übertragbarkeit auf Baumaschinen
- 2015Design and Analysis of an adaptive λ-Tracking Controller for powered Gearshifts in automatic Transmissions
- 2015Entwurf und Evaluierung einer prädiktiven Fahrstrategie auf Basis von Ampel-Fahrzeug-Kommunikationsdaten
- 2015Energiemanagement für eine parallele Hybridfahrzeugarchitektur
- 2014Modellbasierte Optimalsteuerung im Energiemanagement des Kraftfahrzeugs
- 2010Automobilkompetenz der TU Dresden
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document
Phenomenological, Measurement Based LiDAR Sensor Model
Abstract
The advancing automation within the mobility sector poses new challenges. The open parameter space of potential traffic scenarios turns out to be difficult in the development and certification of advanced driver assistance systems. Scenario based, simulative validation of driving functions appears to be a promising solution. Given the assumption that only a fraction of all traffic scenarios is safety critical and should be considered for the evaluation of driver assistance systems, a simulation based selection of test relevant driving scenarios can be carried out. With realistic sensor models available the virtual testing of driver assistance systems is cheaper and faster than conventional test drives. Phenomenological sensor models do not require detailed environment models and therefore compromise accuracy and effort. The objective of this work is the development of a phenomenological LiDAR sensor model that reproduces the actual, measured detection capability of LiDAR sensors. Avoiding empirical radar backscatter cross sections, that strongly distort the detection capability of conventional LiDAR sensor models and mapping the measured detection capability onto the phenomenological LiDAR sensor model promises enhanced model accuracy over traditional phenomenological modeling approaches.
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