Nicolas Scheiner

According to our database, Nicolas Scheiner authored at least 11 papers between 2018 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

Object Detection for Automotive Radar Perception (Machine Learning Approaches for Identifying Moving Road Users in Urban Scenarios)

Nicolas Scheiner

PhD thesis, 2024

2021

The Radar Ghost Dataset - An Evaluation of Ghost Objects in Automotive Radar Data.

Florian Kraus, Nicolas Scheiner, Werner Ritter, Klaus Dietmayer

Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2021

RadarScenes: A Real-World Radar Point Cloud Data Set for Automotive Applications.

Ole Schumann, Markus Hahn, Nicolas Scheiner, Fabio Weishaupt, Julius F. Tilly, Jürgen Dickmann, Christian Wöhler

Proceedings of the 24th IEEE International Conference on Information Fusion, 2021

2020

Using Machine Learning to Detect Ghost Images in Automotive Radar.

Florian Kraus, Nicolas Scheiner, Werner Ritter, Klaus Dietmayer

Proceedings of the 23rd IEEE International Conference on Intelligent Transportation Systems, 2020

Off-the-shelf sensor vs. experimental radar - How much resolution is necessary in automotive radar classification?

Nicolas Scheiner, Ole Schumann, Florian Kraus, Nils Appenrodt, Jürgen Dickmann, Bernhard Sick

Proceedings of the IEEE 23rd International Conference on Information Fusion, 2020

Seeing Around Street Corners: Non-Line-of-Sight Detection and Tracking In-the-Wild Using Doppler Radar.

Nicolas Scheiner, Florian Kraus, Fangyin Wei, Buu Phan, Fahim Mannan, Nils Appenrodt, Werner Ritter, Jürgen Dickmann, Klaus Dietmayer, Bernhard Sick, Felix Heide

Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020

2019

Automated Ground Truth Estimation For Automotive Radar Tracking Applications With Portable GNSS And IMU Devices.

Nicolas Scheiner, Stefan Haag, Nils Appenrodt, Bharanidhar Duraisamy, Jürgen Dickmann, Martin Fritzsche, Bernhard Sick

CoRR, 2019

Automated Ground Truth Estimation of Vulnerable Road Users in Automotive Radar Data Using GNSS.

Nicolas Scheiner, Nils Appenrodt, Jürgen Dickmann, Bernhard Sick

CoRR, 2019

Radar-based Road User Classification and Novelty Detection with Recurrent Neural Network Ensembles.

Nicolas Scheiner, Nils Appenrodt, Jürgen Dickmann, Bernhard Sick

Proceedings of the 2019 IEEE Intelligent Vehicles Symposium, 2019

A Multi-Stage Clustering Framework for Automotive Radar Data.

Nicolas Scheiner, Nils Appenrodt, Jürgen Dickmann, Bernhard Sick

Proceedings of the 2019 IEEE Intelligent Transportation Systems Conference, 2019

2018

Radar-based Feature Design and Multiclass Classification for Road User Recognition.

Nicolas Scheiner, Nils Appenrodt, Jürgen Dickmann, Bernhard Sick

Proceedings of the 2018 IEEE Intelligent Vehicles Symposium, 2018