RCooper: A Real-world Large-scale Dataset for Roadside Cooperative Perception
CVPR 2024(2024)
摘要
The value of roadside perception, which could extend the boundaries of
autonomous driving and traffic management, has gradually become more prominent
and acknowledged in recent years. However, existing roadside perception
approaches only focus on the single-infrastructure sensor system, which cannot
realize a comprehensive understanding of a traffic area because of the limited
sensing range and blind spots. Orienting high-quality roadside perception, we
need Roadside Cooperative Perception (RCooper) to achieve practical
area-coverage roadside perception for restricted traffic areas. Rcooper has its
own domain-specific challenges, but further exploration is hindered due to the
lack of datasets. We hence release the first real-world, large-scale RCooper
dataset to bloom the research on practical roadside cooperative perception,
including detection and tracking. The manually annotated dataset comprises 50k
images and 30k point clouds, including two representative traffic scenes (i.e.,
intersection and corridor). The constructed benchmarks prove the effectiveness
of roadside cooperation perception and demonstrate the direction of further
research. Codes and dataset can be accessed at:
https://github.com/AIR-THU/DAIR-RCooper.
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