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Bosch Street Dataset: A Multi-Modal Dataset with Imaging Radar for Automated Driving

24 June 2024
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
R. Maiwald
Yancong Lin
Ulf Rüegg
Claudius Gläser
Bastian Bischoff
J. Freess
Karsten Haug
Kathrin Klee
Holger Caesar
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Abstract

This paper introduces the Bosch street dataset (BSD), a novel multi-modal large-scale dataset aimed at promoting highly automated driving (HAD) and advanced driver-assistance systems (ADAS) research. Unlike existing datasets, BSD offers a unique integration of high-resolution imaging radar, lidar, and camera sensors, providing unprecedented 360-degree coverage to bridge the current gap in high-resolution radar data availability. Spanning urban, rural, and highway environments, BSD enables detailed exploration into radar-based object detection and sensor fusion techniques. The dataset is aimed at facilitating academic and research collaborations between Bosch and current and future partners. This aims to foster joint efforts in developing cutting-edge HAD and ADAS technologies. The paper describes the dataset's key attributes, including its scalability, radar resolution, and labeling methodology. Key offerings also include initial benchmarks for sensor modalities and a development kit tailored for extensive data analysis and performance evaluation, underscoring our commitment to contributing valuable resources to the HAD and ADAS research community.

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