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DLRAD - A first look on the new vision and mapping benchmark dataset for autonomous driving

Kurz, Franz und Waigand, Daniel und Pekezou Fouopi, Paulin und Vig, Eleonora und Henry, Corentin und Merkle, Nina und Rosenbaum, Dominik und Gstaiger, Veronika und Azimi, Seyedmajid und Auer, Stefan und Reinartz, Peter und Knake-Langhorst, Sascha (2018) DLRAD - A first look on the new vision and mapping benchmark dataset for autonomous driving. ISPRS TC 1 Symposium, 2018-10-10 - 2018-10-12, Karlsruhe. doi: 10.5194/isprs-archives-XLII-1-251-2018.

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Kurzfassung

DLRAD - a new vision and mapping benchmark dataset for autonomous driving is now ready for the development and validation of intelligent driving algorithms. Stationary, mobile, and airborne sensors monitored simultaneously the environment around a reference vehicle, which was driving on urban, suburb and rural roads in and around the city of Braunschweig/Germany. Airborne images were acquired with the DLR 4k sensor system mounted on a helicopter. The DLR reference car FASCarE is equipped with the latest car sensor technology like front/rear radar, ultrasound and laser sensors, optical single and stereo cameras, and GNSS/IMU. Additionally, stationary terrestrial sensors like induction loops, optical mono and stereo cameras, radar and laser scanners monitor defined sections of the path from the ground. The stationary sensors are installed on gantries at main crossings and on pylons. The benchmark path with total length of 156km is divided in an urban road scenario (34km), a rural road scenario (50km), an industrial area scenario (26km), and a motorway scenario (46 km). Simultaneously, the helicopter with the 4k sensor systems follows the reference car by keeping it all the time in the central nadir view. Two 20MPix full frame nadir looking cameras with focal lengths of 50mm and 25mm cover the area around the reference car staggered according to the distance from the reference car with different GSDs of 7cm resp. 14cm. With a focal length of 50mm an area of 320m x 240m is covered assuming a flight height of 500m above ground. With frame rates around 1 Hz, it will be possible to create a 3D reference map and database with the positions of all moving and non-moving objects around the reference car including pedestrians, cyclists and all kinds of vehicles. This database will be augmented with the data from the stationary sensors to have a more detailed view at defined sections. A next crucial step in the construction of the DLRAD benchmark dataset is the annotation of all objects in the reference dataset. The reference vehicle FASCarE is a Volkswagen eGolf. It is equipped with different range detectors as follows: each four rear and front ultrasound sensors for the close range detection < 5m, three front and one rear IBEO laser scanner with range 200m, each two front and rear SMS radar, and one front Bosch radar. Additionally, an optical Bosch camera is installed at the front window and a stereo camera system is installed at the car roof for 3D and object detection purposes. The DLRAD benchmark dataset enables a huge variety of validation capabilities and opens a wide field of possibilities for the development, training and validation of machine learning algorithms in the context of autonomous driving. In this paper, we will present details of the sensor configurations and the acquisition campaign, which had taken place between the 18th July and 20th July 2017 in Braunschweig/Germany. Also, we show a first analysis of the data including the completeness and geometrical quality.

elib-URL des Eintrags:https://elib.dlr.de/120498/
Dokumentart:Konferenzbeitrag (Vortrag)
Titel:DLRAD - A first look on the new vision and mapping benchmark dataset for autonomous driving
Autoren:
AutorenInstitution oder E-Mail-AdresseAutoren-ORCID-iDORCID Put Code
Kurz, Franzfranz.kurz (at) dlr.dehttps://orcid.org/0000-0003-1718-0004NICHT SPEZIFIZIERT
Waigand, Danieldaniel.waigand (at) dlr.deNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Pekezou Fouopi, PaulinPaulin.PekezouFouopi (at) dlr.dehttps://orcid.org/0000-0003-3583-8279NICHT SPEZIFIZIERT
Vig, Eleonoraeleonora.vig (at) dlr.deNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Henry, Corentincorentin.henry (at) dlr.deNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Merkle, Ninanina.merkle (at) dlr.dehttps://orcid.org/0000-0003-4177-1066NICHT SPEZIFIZIERT
Rosenbaum, DominikDominik.Rosenbaum (at) dlr.deNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Gstaiger, VeronikaVeronika.Gstaiger (at) dlr.dehttps://orcid.org/0000-0001-7328-7485NICHT SPEZIFIZIERT
Azimi, SeyedmajidSeyedmajid.Azimi (at) dlr.deNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Auer, StefanStefan.Auer (at) dlr.dehttps://orcid.org/0000-0001-9310-2337139396227
Reinartz, Peterpeter.reinartz (at) dlr.dehttps://orcid.org/0000-0002-8122-1475NICHT SPEZIFIZIERT
Knake-Langhorst, Saschasascha.knake-langhorst (at) dlr.dehttps://orcid.org/0000-0001-7399-0939134542476
Datum:2018
Referierte Publikation:Nein
Open Access:Ja
Gold Open Access:Nein
In SCOPUS:Nein
In ISI Web of Science:Nein
DOI:10.5194/isprs-archives-XLII-1-251-2018
Seitenbereich:Seiten 1-6
Status:veröffentlicht
Stichwörter:airborne camera, vehicle sensors, benchmark dataset, autonomous driving, sensor fusion
Veranstaltungstitel:ISPRS TC 1 Symposium
Veranstaltungsort:Karlsruhe
Veranstaltungsart:internationale Konferenz
Veranstaltungsbeginn:10 Oktober 2018
Veranstaltungsende:12 Oktober 2018
Veranstalter :ISPRS
HGF - Forschungsbereich:Luftfahrt, Raumfahrt und Verkehr
HGF - Programm:Verkehr
HGF - Programmthema:Verkehrsmanagement (alt)
DLR - Schwerpunkt:Verkehr
DLR - Forschungsgebiet:V VM - Verkehrsmanagement
DLR - Teilgebiet (Projekt, Vorhaben):V - Vabene++ (alt)
Standort: Braunschweig , Oberpfaffenhofen
Institute & Einrichtungen:Institut für Methodik der Fernerkundung > Photogrammetrie und Bildanalyse
Institut für Verkehrssystemtechnik > Datenerfassung und Informationsgewinnung
Hinterlegt von: Kurz, Dr.-Ing. Franz
Hinterlegt am:22 Jun 2018 10:24
Letzte Änderung:24 Apr 2024 20:24

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