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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2407.08261 |
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| _version_ | 1866909811059195904 |
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| author | Vosshans, Marcel Baumann, Alexander Drueppel, Matthias Ait-Aider, Omar Mezouar, Youcef Dang, Thao Enzweiler, Markus |
| author_facet | Vosshans, Marcel Baumann, Alexander Drueppel, Matthias Ait-Aider, Omar Mezouar, Youcef Dang, Thao Enzweiler, Markus |
| contents | The increasing complexity of urban environments has underscored the potential of effective collective perception systems. To address these challenges, we present the CoopScenes dataset, a large-scale, multi-scene dataset that provides synchronized sensor data from both the ego-vehicle and the supporting infrastructure.The dataset provides 104 minutes of spatially and temporally synchronized data at 10 Hz, resulting in 62,000 frames. It achieves competitive synchronization with a mean deviation of only 2.3 ms. Additionally the dataset includes a novel procedure for precise registration of point cloud data from the ego-vehicle and infrastructure sensors, automated annotation pipelines, and an open-source anonymization pipeline for faces and license plates. Covering nine diverse scenes with 100 maneuvers, the dataset features scenarios such as public transport hubs, city construction sites, and high-speed rural roads across three cities in the Stuttgart region, Germany. The full dataset amounts to 527 GB of data and is provided in the .4mse format, making it easily accessible through our comprehensive development kit. By providing precise, large-scale data, CoopScenes facilitates research in collective perception, real-time sensor registration, and cooperative intelligent systems for urban mobility, including machine learning-based approaches. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_08261 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | CoopScenes: Multi-Scene Infrastructure and Vehicle Data for Advancing Collective Perception in Autonomous Driving Vosshans, Marcel Baumann, Alexander Drueppel, Matthias Ait-Aider, Omar Mezouar, Youcef Dang, Thao Enzweiler, Markus Robotics The increasing complexity of urban environments has underscored the potential of effective collective perception systems. To address these challenges, we present the CoopScenes dataset, a large-scale, multi-scene dataset that provides synchronized sensor data from both the ego-vehicle and the supporting infrastructure.The dataset provides 104 minutes of spatially and temporally synchronized data at 10 Hz, resulting in 62,000 frames. It achieves competitive synchronization with a mean deviation of only 2.3 ms. Additionally the dataset includes a novel procedure for precise registration of point cloud data from the ego-vehicle and infrastructure sensors, automated annotation pipelines, and an open-source anonymization pipeline for faces and license plates. Covering nine diverse scenes with 100 maneuvers, the dataset features scenarios such as public transport hubs, city construction sites, and high-speed rural roads across three cities in the Stuttgart region, Germany. The full dataset amounts to 527 GB of data and is provided in the .4mse format, making it easily accessible through our comprehensive development kit. By providing precise, large-scale data, CoopScenes facilitates research in collective perception, real-time sensor registration, and cooperative intelligent systems for urban mobility, including machine learning-based approaches. |
| title | CoopScenes: Multi-Scene Infrastructure and Vehicle Data for Advancing Collective Perception in Autonomous Driving |
| topic | Robotics |
| url | https://arxiv.org/abs/2407.08261 |