Radar Artifact Labeling Framework (RALF): Method for Plausible Radar Detections in Datasets

Simon Isele, Simon Isele, Marcel Schilling, Marcel Schilling, Fabian Klein, Sascha Saralajew, J. Zoellner, J. Zoellner

Abstract

Research on localization and perception for Autonomous Driving is mainly focused on camera and LiDAR datasets, rarely on radar data. Manually labeling sparse radar point clouds is challenging. For a dataset generation, we propose the cross sensor Radar Artifact Labeling Framework (RALF). Automatically generated labels for automotive radar data help to cure radar shortcomings like artifacts for the application of artificial intelligence. RALF provides plausibility labels for radar raw detections, distinguishing between artifacts and targets. The optical evaluation backbone consists of a generalized monocular depth image estimation of surround view cameras plus LiDAR scans. Modern car sensor sets of cameras and LiDAR allow to calibrate image-based relative depth information in overlapping sensing areas. K-Nearest Neighbors matching relates the optical perception point cloud with raw radar detections. In parallel, a temporal tracking evaluation part considers the radar detections’ transient behavior. Based on the distance between matches, respecting both sensor and model uncertainties, we propose a plausibility rating of every radar detection. We validate the results by evaluating error metrics on semi-manually labeled ground truth dataset of 3.28·106 points. Besides generating plausible radar detections, the framework enables further labeled low-level radar signal datasets for applications of perception and Autonomous Driving learning tasks.

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Paper Citation


in Harvard Style

Isele S., Schilling M., Klein F., Saralajew S. and Zoellner J. (2021). Radar Artifact Labeling Framework (RALF): Method for Plausible Radar Detections in Datasets. In Proceedings of the 7th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS, ISBN 978-989-758-513-5, pages 22-33. DOI: 10.5220/0010395100220033


in Bibtex Style

@conference{vehits21,
author={Simon Isele and Marcel Schilling and Fabian Klein and Sascha Saralajew and J. Zoellner},
title={Radar Artifact Labeling Framework (RALF): Method for Plausible Radar Detections in Datasets},
booktitle={Proceedings of the 7th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS,},
year={2021},
pages={22-33},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010395100220033},
isbn={978-989-758-513-5},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 7th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS,
TI - Radar Artifact Labeling Framework (RALF): Method for Plausible Radar Detections in Datasets
SN - 978-989-758-513-5
AU - Isele S.
AU - Schilling M.
AU - Klein F.
AU - Saralajew S.
AU - Zoellner J.
PY - 2021
SP - 22
EP - 33
DO - 10.5220/0010395100220033