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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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