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Titel: Learning Motion Primitives Automata for Autonomous Driving Applications
VerfasserIn: Pedrosa, Matheus V. A.
Schneider, Tristan
Flaßkamp, Kathrin
Sprache: Englisch
Titel: Mathematical and Computational Applications
Bandnummer: 27
Heft: 4
Verlag/Plattform: MDPI
Erscheinungsjahr: 2022
Freie Schlagwörter: dynamical systems
control
symmetry
trajectory planning
motion primitives
maneuver automata
clustering
data-based modeling
autonomous driving
DDC-Sachgruppe: 500 Naturwissenschaften
Dokumenttyp: Journalartikel / Zeitschriftenartikel
Abstract: Motion planning methods often rely on libraries of primitives. The selection of primitives is then crucial for assuring feasible solutions and good performance within the motion planner. In the literature, the library is usually designed by either learning from demonstration, relying entirely on data, or by model-based approaches, with the advantage of exploiting the dynamical system’s property, e.g., symmetries. In this work, we propose a method combining data with a dynamical model to optimally select primitives. The library is designed based on primitives with highest occurrences within the data set, while Lie group symmetries from a model are analysed in the available data to allow for structure-exploiting primitives. We illustrate our technique in an autonomous driving application. Primitives are identified based on data from human driving, with the freedom to build libraries of different sizes as a parameter of choice. We also compare the extracted library with a custom selection of primitives regarding the performance of obtained solutions for a street layout based on a real-world scenario.
DOI der Erstveröffentlichung: 10.3390/mca27040054
Link zu diesem Datensatz: urn:nbn:de:bsz:291--ds-370802
hdl:20.500.11880/33661
http://dx.doi.org/10.22028/D291-37080
ISSN: 2297-8747
Datum des Eintrags: 26-Aug-2022
Fakultät: NT - Naturwissenschaftlich- Technische Fakultät
Fachrichtung: NT - Systems Engineering
Professur: NT - Univ.-Prof. Dr. Kathrin Flaßkamp
Sammlung:SciDok - Der Wissenschaftsserver der Universität des Saarlandes

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