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doi:10.22028/D291-38746
Titel: | Parallel Multi-Hypothesis Algorithm for Criticality Estimation in Traffic and Collision Avoidance |
VerfasserIn: | Morales, Eduardo Sanchez Membarth, Richard Gaull, Andreas Slusallek, Philipp Dirndorfer, Tobias Kammenhuber, Alexander Lauer, Christoph Botsch, Michael |
Sprache: | Englisch |
Titel: | IV19 : 30th IEEE Intelligent Vehicles Symposium : 9-12 June 2019, Paris |
Seiten: | 2164-2171 |
Verlag/Plattform: | IEEE |
Erscheinungsjahr: | 2019 |
Freie Schlagwörter: | parallel multihypothesis algorithm criticality estimation collision avoidance vehicle active safety complex criticality predictions multiobject traffic scenarios automotive applications driver assistance systems collision prevention fall-back systems autonomous vehicles parallelizable architecture pedestrians trajectory combinations vehicle safety-systems autonomous driving applications embedded system complex traffic scenario Drive PX 2 dynamic objects time 21.0 ms |
DDC-Sachgruppe: | 004 Informatik |
Dokumenttyp: | Konferenzbeitrag (in einem Konferenzband / InProceedings erschienener Beitrag) |
Abstract: | Due to the current developments towards autonomous driving and vehicle active safety, there is an increasing necessity for algorithms that are able to perform complex criticality predictions in real-time. Being able to process multi-object traffic scenarios aids the implementation of a variety of automotive applications such as driver assistance systems for collision prevention and mitigation as well as fall-back systems for autonomous vehicles. We present a fully model-based algorithm with a parallelizable architecture. The proposed algorithm can evaluate the criticality of complex, multi-modal (vehicles and pedestrians) traffic scenarios by simulating millions of trajectory combinations and detecting collisions between objects. The algorithm is able to estimate upcoming criticality at very early stages, demonstrating its potential for vehicle safety-systems and autonomous driving applications. An implementation on an embedded system in a test vehicle proves in a prototypical manner the compatibility of the algorithm with the hardware possibilities of modern cars. For a complex traffic scenario with 11 dynamic objects, more than 86 million pose combinations are evaluated in 21 ms on the GPU of a Drive PX 2. |
DOI der Erstveröffentlichung: | 10.1109/IVS.2019.8814015 |
URL der Erstveröffentlichung: | https://doi.org/10.1109/IVS.2019.8814015 |
Link zu diesem Datensatz: | urn:nbn:de:bsz:291--ds-387469 hdl:20.500.11880/34907 http://dx.doi.org/10.22028/D291-38746 |
ISBN: | 978-1-7281-0560-4 |
ISSN: | 2473-2001 |
Datum des Eintrags: | 18-Jan-2023 |
Fakultät: | MI - Fakultät für Mathematik und Informatik |
Fachrichtung: | MI - Informatik |
Professur: | MI - Prof. Dr. Philipp Slusallek |
Sammlung: | SciDok - Der Wissenschaftsserver der Universität des Saarlandes |
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