CATS-V2V: A Real-World Vehicle-to-Vehicle Cooperative Perception Dataset with Complex Adverse Traffic Scenarios
Vehicle-to-Vehicle (V2V) cooperative perception has great potential to enhance autonomous driving performance by overcoming perception limitations in complex adverse traffic scenarios (CATS). Meanwhile, data serves as the fundamental infrastructure for modern autonomous driving AI. However, due to stringent data collection requirements, existing datasets focus primarily on ordinary traffic scenarios, constraining the benefits of cooperative perception. To address this challenge, we introduce CATS-V2V, the first-of-its-kind real-world dataset for V2V cooperative perception under complex adverse traffic scenarios. The dataset was collected by two hardware time-synchronized vehicles, covering 10 weather and lighting conditions across 10 diverse locations. The 100-clip dataset includes 60K frames of 10 Hz LiDAR point clouds and 1.26M multi-view 30 Hz camera images, along with 750K anonymized yet high-precision RTK-fixed GNSS and IMU records. Correspondingly, we provide time-consistent 3D bounding box annotations for objects, as well as static scenes to construct a 4D BEV representation. On this basis, we propose a target-based temporal alignment method, ensuring that all objects are precisely aligned across all sensor modalities. We hope that CATS-V2V, the largest-scale, most supportive, and highest-quality dataset of its kind to date, will benefit the autonomous driving community in related tasks.
691a97986bfd5965c0fd3489
Hangyu Li
691a97986bfd5965c0fd348a
Bofeng Cao
691a97986bfd5965c0fd348b
Zhaohui Liang
691a97986bfd5965c0fd348c
Wuzhen Li
691a97986bfd5965c0fd348d
Juyoung Oh
691a97986bfd5965c0fd348e
Yuxuan Chen
691a97986bfd5965c0fd348f
Shixiao Liang
691a97986bfd5965c0fd3490
Hang Zhou
691a97986bfd5965c0fd3491
Chengyuan Ma
691a97986bfd5965c0fd3492
Jiaxi Liu
691a97986bfd5965c0fd3493
Zheng Li
691a97986bfd5965c0fd3494
Peng Zhang
691a97986bfd5965c0fd3495
KeKe Long
691a97986bfd5965c0fd3496
Maolin Liu
691a97986bfd5965c0fd3497
Jackson Jiang
691a97986bfd5965c0fd3498
Chunlei Yu
691a97986bfd5965c0fd3499
Shengxiang Liu
691a97986bfd5965c0fd349a
Hongkai Yu
691a97986bfd5965c0fd349b
Xiaopeng Li
CATS-V2V: A Real-World Vehicle-to-Vehicle Cooperative Perception Dataset with Complex Adverse Traffic Scenarios
Vehicle-to-Vehicle (V2V) cooperative perception has great potential to enhance autonomous driving performance by overcoming perception limitations in complex adverse traffic scenarios (CATS). Meanwhile, data serves as the fundamental infrastructure for modern autonomous driving AI. However, due to stringent data collection requirements, existing datasets focus primarily on ordinary traffic scenarios, constraining the benefits of cooperative perception. To address this challenge, we introduce CATS-V2V, the first-of-its-kind real-world dataset for V2V cooperative perception under complex adverse traffic scenarios. The dataset was collected by two hardware time-synchronized vehicles, covering 10 weather and lighting conditions across 10 diverse locations. The 100-clip dataset includes 60K frames of 10 Hz LiDAR point clouds and 1.26M multi-view 30 Hz camera images, along with 750K anonymized yet high-precision RTK-fixed GNSS and IMU records. Correspondingly, we provide time-consistent 3D bounding box annotations for objects, as well as static scenes to construct a 4D BEV representation. On this basis, we propose a target-based temporal alignment method, ensuring that all objects are precisely aligned across all sensor modalities. We hope that CATS-V2V, the largest-scale, most supportive, and highest-quality dataset of its kind to date, will benefit the autonomous driving community in related tasks.
CATS-V2V is a new real-world dataset for V2V cooperative perception in complex adverse traffic scenarios, providing comprehensive sensor data and precise temporal alignment.
LiDAR point clouds multi-view camera images RTK-fixed GNSS IMU records 3D bounding box annotations 4D BEV representation target-based temporal alignment