Concept for Automatic Annotation of Automotive Radar Data Using AI-Segmented Camera and Lidar Reference Data

Heidbrink M, Sura O, Rangaraj VK, Reinecke M, Hoffmann M, Vossiek M (2024)

Publication Type: Conference contribution

Publication year: 2024

Publisher: IEEE

City/Town: New York City

Pages Range: 292-295

Conference Proceedings Title: 2024 21st European Radar Conference (EuRAD)

Event location: Paris

DOI: 10.23919/EuRAD61604.2024.10734878

Abstract

This work presents a concept for automatic annotation of automotive radar data by combining AI-segmented camera images and 3D lidar point clouds. The AI-interpreted images are used to detect objects or features, which are then localized within the lidar point cloud. This enables a precise labeling of various radar data representations. The concept’s effectiveness is validated on a test vehicle equipped with an automotive radar and off-the-shelf camera and lidar sensors. As this concept relies on standard processing techniques, it integrates seamlessly into other setups and is beneficial along the complete radar development chain.

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APA:

Heidbrink, M., Sura, O., Rangaraj, V.K., Reinecke, M., Hoffmann, M., & Vossiek, M. (2024). Concept for Automatic Annotation of Automotive Radar Data Using AI-Segmented Camera and Lidar Reference Data. In 2024 21st European Radar Conference (EuRAD) (pp. 292-295). Paris, FR: New York City: IEEE.

MLA:

Heidbrink, Max, et al. "Concept for Automatic Annotation of Automotive Radar Data Using AI-Segmented Camera and Lidar Reference Data." Proceedings of the 2024 21st European Radar Conference (EuRAD), Paris New York City: IEEE, 2024. 292-295.

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