Summary of Bosch Street Dataset: a Multi-modal Dataset with Imaging Radar For Automated Driving, by Karim Armanious et al.

by Karim Armanious, Maurice Quach, Michael Ulrich, Timo Winterling, Johannes Friesen, Sascha Braun, Daniel Jenet, Yuri Feldman, Eitan Kosman, Philipp Rapp, Volker Fischer, Marc Sons, Lukas Kohns, Daniel Eckstein, Daniela Egbert, Simone Letsch, Corinna Voege, Felix Huttner, Alexander Bartler, Robert Maiwald, Yancong Lin, Ulf Rüegg, Claudius Gläser, Bastian Bischoff, Jascha Freess, Karsten Haug, Kathrin Klee, Holger Caesar

First submitted to arxiv on: 24 Jun 2024

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Summary difficulty	Written by	Summary
High	Paper authors	High Difficulty Summary Read the original abstract here
Medium	GrooveSquid.com (original content)	Medium Difficulty Summary The paper introduces the Bosch street dataset (BSD), a multi-modal large-scale dataset designed to promote highly automated driving (HAD) and advanced driver-assistance systems (ADAS) research. The dataset integrates high-resolution imaging radar, lidar, and camera sensors for 360-degree coverage, filling the gap in high-resolution radar data availability. It spans urban, rural, and highway environments, enabling exploration of radar-based object detection and sensor fusion techniques. The paper describes the dataset’s key attributes, including scalability, radar resolution, and labeling methodology.
Low	GrooveSquid.com (original content)	Low Difficulty Summary This dataset is aimed at facilitating research collaborations between Bosch and partners to develop cutting-edge HAD and ADAS technologies. It offers initial benchmarks for sensor modalities and a development kit for data analysis and performance evaluation.