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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.…
Document Type: | Conference Proceeding |
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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 WagnerGND, 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): | Urheberrechtlich geschützt |
ArXiv Id: | http://arxiv.org/abs/1903.08495 |