148 citations · 340 across the 8 of their papers we have counts for
8 papers
Landmark-based Vehicle Self-Localization Using Automotive Polarimetric Radars
Fabio Weishaupt, Julius F. Tilly, Nils Appenrodt +3
Automotive self-localization is an essential task for any automated driving function. This means that the vehicle has to reliably know its position and orientation with an accuracy…
Off-the-shelf sensor vs. experimental radar -- How much resolution is necessary in automotive radar classification?
Nicolas Scheiner, Ole Schumann, Florian Kraus +3
Radar-based road user detection is an important topic in the context of autonomous driving applications. The resolution of conventional automotive radar sensors results in a sparse…
Seeing Around Street Corners: Non-Line-of-Sight Detection and Tracking In-the-Wild Using Doppler Radar
Nicolas Scheiner, Florian Kraus, Fangyin Wei +8
Conventional sensor systems record information about directly visible objects, whereas occluded scene components are considered lost in the measurement process. Non-line-of-sight (…
A Multi-Stage Clustering Framework for Automotive Radar Data
Nicolas Scheiner, Nils Appenrodt, Jürgen Dickmann +1
Radar sensors provide a unique method for executing environmental perception tasks towards autonomous driving. Especially their capability to perform well in adverse weather condit…
Automated Ground Truth Estimation For Automotive Radar Tracking Applications With Portable GNSS And IMU Devices
Nicolas Scheiner, Stefan Haag, Nils Appenrodt +4
Baseline generation for tracking applications is a difficult task when working with real world radar data. Data sparsity usually only allows an indirect way of estimating the origi…
Radar-based Road User Classification and Novelty Detection with Recurrent Neural Network Ensembles
Nicolas Scheiner, Nils Appenrodt, Jürgen Dickmann +1
Radar-based road user classification is an important yet still challenging task towards autonomous driving applications. The resolution of conventional automotive radar sensors res…
Radar-based Feature Design and Multiclass Classification for Road User Recognition
Nicolas Scheiner, Nils Appenrodt, Jürgen Dickmann +1
The classification of individual traffic participants is a complex task, especially for challenging scenarios with multiple road users or under bad weather conditions. Radar sensor…
Automated Ground Truth Estimation of Vulnerable Road Users in Automotive Radar Data Using GNSS
Nicolas Scheiner, Nils Appenrodt, Jürgen Dickmann +1
Annotating automotive radar data is a difficult task. This article presents an automated way of acquiring data labels which uses a highly accurate and portable global navigation sa…