Polar Occupancy Map - A Compact Traffic Representation for Deep Learning Scenario Classification

Halil Beglerovic; Jonas Ruebsam; Steffen Metzner; Martin Horn

doi:10.1109/ITSC.2019.8916947

Polar Occupancy Map - A Compact Traffic Representation for Deep Learning Scenario Classification

Halil Beglerovic, Jonas Ruebsam, Steffen Metzner, Martin Horn

Institut für Regelungs- und Automatisierungstechnik (4430)

Publikation: Beitrag in Buch/Bericht/Konferenzband › Beitrag in einem Konferenzband › Begutachtung

Abstract

In order to accelerate development, testing and verification of automated vehicles, it is crucial to classify a wide range of driving scenarios. Scenario classification is usually done by rule-based algorithms or even manual video or signal inspection. A promising alternative is to use machine learning and let neural networks extract the relevant classification features. Since inputs to neural networks need to have a fixed size, an abstract representation of the driving scenario is necessary. In this paper, a scenario representation that captures the dynamic traffic behavior, without loss of relevant information, is presented.

Originalsprache	englisch
Titel	2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019
Herausgeber (Verlag)	Institute of Electrical and Electronics Engineers
Seiten	4197-4203
Seitenumfang	7
ISBN (elektronisch)	9781538670248
DOIs	https://doi.org/10.1109/ITSC.2019.8916947
Publikationsstatus	Veröffentlicht - 1 Okt. 2019
Veranstaltung	22nd IEEE International Conference on Intelligent Transportation Systems: ITSC 2019 - Auckland, Neuseeland Dauer: 27 Okt. 2019 → 30 Okt. 2019 https://www.itsc2019.org/

Publikationsreihe

Name	2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019

Konferenz

Konferenz	22nd IEEE International Conference on Intelligent Transportation Systems
Kurztitel	ITSC 2019
Land/Gebiet	Neuseeland
Ort	Auckland
Zeitraum	27/10/19 → 30/10/19
Internetadresse	https://www.itsc2019.org/

ASJC Scopus subject areas

Artificial intelligence
Managementlehre und Operations Resarch
Instrumentierung
Verkehr

Zugriff auf Dokument

10.1109/ITSC.2019.8916947

Andere Dateien und Links

http://www.scopus.com/inward/record.url?scp=85076808015&partnerID=8YFLogxK

Dieses zitieren

Beglerovic, H., Ruebsam, J., Metzner, S., & Horn, M. (2019). Polar Occupancy Map - A Compact Traffic Representation for Deep Learning Scenario Classification. in 2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019 (S. 4197-4203). Artikel 8916947 (2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ITSC.2019.8916947

Polar Occupancy Map - A Compact Traffic Representation for Deep Learning Scenario Classification. / Beglerovic, Halil; Ruebsam, Jonas; Metzner, Steffen et al.
2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019. Institute of Electrical and Electronics Engineers, 2019. S. 4197-4203 8916947 (2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019).

Publikation: Beitrag in Buch/Bericht/Konferenzband › Beitrag in einem Konferenzband › Begutachtung

Beglerovic, H, Ruebsam, J, Metzner, S & Horn, M 2019, Polar Occupancy Map - A Compact Traffic Representation for Deep Learning Scenario Classification. in 2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019., 8916947, 2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019, Institute of Electrical and Electronics Engineers, S. 4197-4203, 22nd IEEE International Conference on Intelligent Transportation Systems, Auckland, Neuseeland, 27/10/19. https://doi.org/10.1109/ITSC.2019.8916947

Beglerovic H, Ruebsam J, Metzner S, Horn M. Polar Occupancy Map - A Compact Traffic Representation for Deep Learning Scenario Classification. in 2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019. Institute of Electrical and Electronics Engineers. 2019. S. 4197-4203. 8916947. (2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019). doi: 10.1109/ITSC.2019.8916947

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abstract = "In order to accelerate development, testing and verification of automated vehicles, it is crucial to classify a wide range of driving scenarios. Scenario classification is usually done by rule-based algorithms or even manual video or signal inspection. A promising alternative is to use machine learning and let neural networks extract the relevant classification features. Since inputs to neural networks need to have a fixed size, an abstract representation of the driving scenario is necessary. In this paper, a scenario representation that captures the dynamic traffic behavior, without loss of relevant information, is presented.",

author = "Halil Beglerovic and Jonas Ruebsam and Steffen Metzner and Martin Horn",

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Polar Occupancy Map - A Compact Traffic Representation for Deep Learning Scenario Classification

Abstract

Publikationsreihe

Konferenz

ASJC Scopus subject areas

Zugriff auf Dokument

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