Volltext-Downloads (blau) und Frontdoor-Views (grau)
  • search hit 1 of 1
Back to Result List

Object Detection for the Audi Autonomous Driving Cup

  • One of the challenges for autonomous driving in general is to detect objects in the car's camera images. In the Audi Autonomous Driving Cup (AADC), among those objects are other cars, adult and child pedestrians and emergency vehicle lighting. We show that with recent deep learning networks we are able to detect these objects reliably on the limited Hardware of the model cars. Also, the same deepOne of the challenges for autonomous driving in general is to detect objects in the car's camera images. In the Audi Autonomous Driving Cup (AADC), among those objects are other cars, adult and child pedestrians and emergency vehicle lighting. We show that with recent deep learning networks we are able to detect these objects reliably on the limited Hardware of the model cars. Also, the same deep network is used to detect road features like mid lines, stop lines and even complete crossings. Best results are achieved using Faster R-CNN with Inception v2 showing an overall accuracy of 0.84 at 7 Hz.show moreshow less

Export metadata

Additional Services

Search Google Scholar

Statistics

frontdoor_oas
Metadaten
Document Type:Conference Proceeding
Conference Type:Konferenzartikel
Zitierlink: https://opus.hs-offenburg.de/3951
Bibliografische Angaben
Title (English):Object Detection for the Audi Autonomous Driving Cup
Conference:The Upper-Rhine Artificial Intelligence Symposium (UR-AI 2019), Offenburg, March 13, 2019
Author:Felix WagnerStaff MemberGND, Christoph Lehmann, Klaus DorerStaff MemberORCiDGND
Year of Publication:2019
Creating Corporation:Hochschule Karlsruhe
Contributing Corporation:Hochschule Offenburg
Page Number:4
First Page:43
Last Page:46
Parent Title (English):Artificial Intelligence. From Research To Application
Editor:Andreas Christ, Franz Quint
ISBN:978-3-9820756-0-0 (Print)
ISBN:978-3-9820756-1-7 (eBook)
Language:English
Inhaltliche Informationen
Institutes:Fakultät Elektrotechnik, Medizintechnik und Informatik (EMI) (ab 04/2019)
Institutes:Bibliografie
Projekte / Magma Offenburg
Formale Angaben
Open Access: Open Access 
Licence (German):License LogoUrheberrechtlich geschützt
ArXiv Id:http://arxiv.org/abs/1903.08495