A Method for the Accumulation of Forward-Looking SAR Images in Automotive Applications - Reinecke, Marc; Noegel, Theresa; Heidbrink, Max; Sura, Oliver; Hoffmann, Marcel; Gulden, Peter; Vossiek, Martin (2024 21st European Radar Conference (EuRAD), 2024)
In the automotive domain, Forward-Looking Synthetic Aperture Radar (FL-SAR) systems can provide environmental perception, overcoming visibility limitations and integration constraints. However, synthesizing high-resolution cumulative maps from individual FL-SAR snapshots can be challenging, because varying target angles and, thus, angular resolutions between snapshots have to be considered. The presented method addresses this challenge by dynamically weighting and accumulating multiple snapshots, enabling the trade-off between prioritizing measurements with higher resolutions and obscurity issues. Our approach rectifies resolution inconsistencies inherent in FL-SAR systems, enhancing the quality of cumulative maps. We provide an overview of FL-SAR measurements and achievable resolution gains compared to static imaging methods. Our measurement results demonstrate the effectiveness of the algorithm in generating seamless maps, which was validated in real-world scenarios. This research aims to advance the role of FL-SAR as a reliable tool for environmental perception and mapping in automotive application

Concept for Automatic Annotation of Automotive Radar Data Using AI-Segmented Camera and Lidar Reference Data - Heidbrink, Max; Sura, Oliver; Rangaraj, Vinoth Kumar; Reinecke, Marc; Hoffmann, Marcel; Vossiek, Martin (2024 21st European Radar Conference (EuRAD), 2024)
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 chai

Implementation of 3D Automotive SAR Imaging - Noegel, Theresa; Reinecke, Marc; Schwenger, Lars; Heidbrink, Max; Hoffmann, Marcel; Vossiek, Martin (2024 21st European Radar Conference (EuRAD), 2024)
Autonomous vehicles demand a precise environmental understanding and are often reliant on radar technology for distance measurement and velocity evaluation. However, automotive radars suffer from poor angular resolution due to their limited aperture size. Synthetic Aperture Radar (SAR) mitigates this limitation by leveraging the vehicle’s movement to synthesize a larger aperture, which significantly enhances the angular resolution. While SAR has been utilized for 2D imaging in the automotive field, this work presents a novel approach to generate 3D SAR images by rotating a typical automotive radar sensor to obtain a real aperture in the elevation direction. By conventionally synthesizing the azimuth aperture through vehicle movement, a complete 3D SAR reconstruction of the environment becomes feasible. Real-world measurement results validate the theoretical expectations and demonstrate that additional height information can be provided. Based on these findings, we discuss the identified challenges, define measures to reduce the computational complexity, and derive a design proposal for a radar system suited for 3D automotive SAR application

On the Advantages of Coherent Automotive Radar Networks for Occupancy Gridmapping - Sura, Oliver; Euler, Mario; Heidbrink, Max; Hoffmann, Marcel; Gulden, Peter; Vossiek, Martin (2024 21st European Radar Conference (EuRAD), 2024)
Autonomous driving requires a robust and detailed perception of the environment even under harsh environmental conditions. Radar sensors can play an integral part in reaching this goal. However, compared to lidar, radar data suffer from lower angular resolution and sparser and noisier point clouds. While occupancy gridmapping has proven to be a valuable mapping tool for clutter-prone point clouds, coherent radar networks provide a promising solution for high-resolution radar imaging. Consequently, this paper evaluates the use of a coherent radar network in combination with occupancy gridmapping to demonstrate the benefit of a coherent bistatic radar network consisting of two stations compared to incoherently operating radar systems. It is demonstrated that the occupancy gridmaps obtained with the coherent network are superior to the standard incoherent system in terms of resolution, number of detected targets, and vectorial velocity estimatio