Bosch Street Dataset: A Multi-Modal Dataset with Imaging Radar for Automated Driving
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Abstract (arXiv)
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.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 20 of its 31 references that are in the map. First posted on arXiv 24 Jun 2024.
Builds on
20 of 31 references.
References · 20
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- 2019The Oxford Radar RobotCar Dataset: A Radar Extension to the Oxford RobotCar Datasetcited by 133
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- 2021Raw High-Definition Radar for Multi-Task Learningcited by 69
8 more
- 2021RadarScenes: A Real-World Radar Point Cloud Data Set for Automotive Applicationscited by 61
- 2022Improved Orientation Estimation and Detection with Hybrid Object Detection Networks for Automotive Radarcited by 33
- 2023Dual Radar: A Multi-modal Dataset with Dual 4D Radar for Autonomous Drivingcited by 32
- 2022aiMotive Dataset: A Multimodal Dataset for Robust Autonomous Driving with Long-Range Perceptioncited by 29
- 2022Pointillism: Accurate 3D bounding box estimation with multi-radarscited by 22
- 2021PixSet : An Opportunity for 3D Computer Vision to Go Beyond Point Clouds With a Full-Waveform LiDAR Datasetcited by 20
- 2024The Radar Ghost Dataset -- An Evaluation of Ghost Objects in Automotive Radar Datacited by 4
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Built on by
Citing papers per year: 2024: 1; 2025: 6.
7 papers.
- 2024SpaRC: Sparse Radar-Camera Fusion for 3D Object Detectioncited by 8
- 2025Towards Foundational Models for Single-Chip Radarcited by 6
- 20254D mmWave Radar for Sensing Enhancement in Adverse Environments: Advances and Challengescited by 4
- 2025nuScenes Revisited: Progress and Challenges in Autonomous Drivingcited by 2
- 2025Large Model Enabled Embodied Intelligence for 6G Integrated Perception, Communication, and Computation Networkcited by 2
- 2025Class-Aware PillarMix: Can Mixed Sample Data Augmentation Enhance 3D Object Detection with Radar Point Clouds?cited by 0
- 2025REHEARSE-3D: A Multi-modal Emulated Rain Dataset for 3D Point Cloud De-rainingcited by 0
- 2025Large Model Enabled Embodied Intelligence for 6G Integrated Perception, Communication, and Computation Network17 Dec 2025
- 2025nuScenes Revisited: Progress and Challenges in Autonomous Driving2 Dec 2025
- 2025Towards Foundational Models for Single-Chip Radar15 Sep 2025
- 2025REHEARSE-3D: A Multi-modal Emulated Rain Dataset for 3D Point Cloud De-raining30 Apr 2025
- 20254D mmWave Radar for Sensing Enhancement in Adverse Environments: Advances and Challenges31 Mar 2025
- 2025Class-Aware PillarMix: Can Mixed Sample Data Augmentation Enhance 3D Object Detection with Radar Point Clouds?4 Mar 2025
- 2024SpaRC: Sparse Radar-Camera Fusion for 3D Object Detection29 Nov 2024