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Nicolas Scheiner
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2020 – today
- 2024
- [b1]Nicolas Scheiner:
Object Detection for Automotive Radar Perception (Machine Learning Approaches for Identifying Moving Road Users in Urban Scenarios). Kassel University, Germany, 2024 - [i10]Florian Kraus, Nicolas Scheiner, Werner Ritter, Klaus Dietmayer:
The Radar Ghost Dataset - An Evaluation of Ghost Objects in Automotive Radar Data. CoRR abs/2404.01437 (2024) - 2021
- [c8]Ole Schumann, Markus Hahn, Nicolas Scheiner, Fabio Weishaupt, Julius F. Tilly, Jürgen Dickmann, Christian Wöhler:
RadarScenes: A Real-World Radar Point Cloud Data Set for Automotive Applications. FUSION 2021: 1-8 - [c7]Florian Kraus, Nicolas Scheiner, Werner Ritter, Klaus Dietmayer:
The Radar Ghost Dataset - An Evaluation of Ghost Objects in Automotive Radar Data. IROS 2021: 8570-8577 - [i9]Ole Schumann, Markus Hahn, Nicolas Scheiner, Fabio Weishaupt, Julius F. Tilly, Jürgen Dickmann, Christian Wöhler:
RadarScenes: A Real-World Radar Point Cloud Data Set for Automotive Applications. CoRR abs/2104.02493 (2021) - 2020
- [c6]Nicolas Scheiner, Florian Kraus, Fangyin Wei, Buu Phan, Fahim Mannan, Nils Appenrodt, Werner Ritter, Jürgen Dickmann, Klaus Dietmayer, Bernhard Sick
, Felix Heide:
Seeing Around Street Corners: Non-Line-of-Sight Detection and Tracking In-the-Wild Using Doppler Radar. CVPR 2020: 2065-2074 - [c5]Nicolas Scheiner, Ole Schumann
Off-the-shelf sensor vs. experimental radar - How much resolution is necessary in automotive radar classification? FUSION 2020: 1-8 - [c4]Florian Kraus, Nicolas Scheiner, Werner Ritter, Klaus Dietmayer:
Using Machine Learning to Detect Ghost Images in Automotive Radar. ITSC 2020: 1-7 - [i8]Nicolas Scheiner, Ole Schumann, Florian Kraus, Nils Appenrodt, Jürgen Dickmann, Bernhard Sick:
Off-the-shelf sensor vs. experimental radar - How much resolution is necessary in automotive radar classification? CoRR abs/2006.05485 (2020) - [i7]Florian Kraus, Nicolas Scheiner, Werner Ritter, Klaus Dietmayer:
Using Machine Learning to Detect Ghost Images in Automotive Radar. CoRR abs/2007.05280 (2020)
2010 – 2019
- 2019
- [c3]Nicolas Scheiner
A Multi-Stage Clustering Framework for Automotive Radar Data. ITSC 2019: 2060-2067 - [c2]Nicolas Scheiner
Radar-based Road User Classification and Novelty Detection with Recurrent Neural Network Ensembles. IV 2019: 722-729 - [i6]Nicolas Scheiner, Nils Appenrodt, Jürgen Dickmann, Bernhard Sick:
Automated Ground Truth Estimation of Vulnerable Road Users in Automotive Radar Data Using GNSS. CoRR abs/1905.11219 (2019) - [i5]Nicolas Scheiner, Nils Appenrodt, Jürgen Dickmann, Bernhard Sick:
Radar-based Feature Design and Multiclass Classification for Road User Recognition. CoRR abs/1905.11256 (2019) - [i4]Nicolas Scheiner, Nils Appenrodt, Jürgen Dickmann, Bernhard Sick:
Radar-based Road User Classification and Novelty Detection with Recurrent Neural Network Ensembles. CoRR abs/1905.11703 (2019) - [i3]Nicolas Scheiner, Stefan Haag, Nils Appenrodt, Bharanidhar Duraisamy, Jürgen Dickmann, Martin Fritzsche, Bernhard Sick:
Automated Ground Truth Estimation For Automotive Radar Tracking Applications With Portable GNSS And IMU Devices. CoRR abs/1905.11987 (2019) - [i2]Nicolas Scheiner, Nils Appenrodt, Jürgen Dickmann, Bernhard Sick:
A Multi-Stage Clustering Framework for Automotive Radar Data. CoRR abs/1907.03511 (2019) - [i1]Nicolas Scheiner
Seeing Around Street Corners: Non-Line-of-Sight Detection and Tracking In-the-Wild Using Doppler Radar. CoRR abs/1912.06613 (2019) - 2018
- [c1]Nicolas Scheiner
Radar-based Feature Design and Multiclass Classification for Road User Recognition. Intelligent Vehicles Symposium 2018: 779-786
Coauthor Index
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