- Location
-
Deutsche Nationalbibliothek Frankfurt am Main
- Extent
-
Online-Ressource
- Language
-
Englisch
- Keyword
-
Segmentierung
- Event
-
Veröffentlichung
- (where)
-
Chemnitz
- (who)
-
Technische Universität Chemnitz
- (when)
-
2020
- Event
-
Veröffentlichung
- (where)
-
Chemnitz
- (who)
-
Technische Universität Chemnitz
- (when)
-
2020
- Creator
-
Ngo, Quang Thanh
- Contributor
- URN
-
urn:nbn:de:bsz:ch1-qucosa2-731119
- Rights
-
Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
15.08.2025, 7:31 AM CEST
Data provider
This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Ngo, Quang Thanh
- Hardt, Wolfram
- Lindner, Philipp
- Technische Universität Chemnitz
Time of origin
- 2020
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Object-level fusion for surround environment perception in automated driving applications
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Object-level fusion for surround environment perception in automated driving applications
Video-Based Environment Perception for Automated Driving using Deep Neural Networks
Reliability Analysis of Multi-Sensor Perception Systems of Automated Driving Functions
Environment perception simulation for radar stimulation in automated driving function testing
Validating Machine Learning-based Highly Automated Driving Functions by Diversity
Essential properties for safe behaviour of a perception function in automated driving
A Review of Testing Object-Based Environment Perception for Safe Automated Driving
Virtual Sensorics: Simulated Environmental Perception for Automated Driving Systems
Safety Assessment of Environment Perception in Automated Driving Vehicles
Comfort in Automated Driving : Analysis of Driving Style Preference in Automated Driving
Object-level fusion for surround environment perception in automated driving applications
Traffic Scene Perception for Automated Driving with Top-View Grid Maps
Object-level fusion for surround environment perception in automated driving applications
Video-Based Environment Perception for Automated Driving using Deep Neural Networks
Reliability Analysis of Multi-Sensor Perception Systems of Automated Driving Functions
Environment perception simulation for radar stimulation in automated driving function testing
Validating Machine Learning-based Highly Automated Driving Functions by Diversity
Essential properties for safe behaviour of a perception function in automated driving