The first open-source collaborative perception dataset focused on adverse weather conditions.



Key Features

  • Simulated in CARLA with OpenCDA.
  • 24,087 frames, 890,127 annotations.
  • Six object categories:
    • Pedestrians
    • Bicycles
    • Motorcycles
    • Vans
    • Trucks
    • Vehicles.
  • Annotation schema compatible with OPV2V.
  • 110 scenarios including:
    • 5 road configurations.
    • 11 weather/daytime conditions.
    • 2 density settings.
  • Every scenario features collaboration between 5 viewpoints:
    • The main vehicle (Ego).
    • 2 Roadside Units (RSU 1 and RSU 2).
    • 2 Connected Vehicles (Car 1 and Car 2).

Road Configurations

Each configuration was chosen from real-world data accounting for challenging conditions involving adverse weather and poor visibility.


Weather Conditions

Having a balanced distribution of diverse conditions, Adver-City is the first synthetic collaborative perception dataset to introduce glare.


Density Settings

The varying number of objects in each setting enables novel experimentation with different levels of occlusion.


Sensor Suite

Balancing performance and real-world feasibility, our sensor suite is based on configurations commonly used in practice and enables Sim2Real approaches.

SensorsDetails
4x RGB camerasResolution: 1920 x 1080 pixels, HFOV: 100 degrees
4x Semantic camerasResolution: 1920 x 1080 pixels, HFOV: 100 degrees
1x 3D LiDARChannels: 32 channels, Points Per Second: 1.2M, Frequency: 10 Hz, Range: 200 meters, VFOV: -25 to 15 degrees
GNSS & IMUStandard deviation: 3e-6 rad (latitude and longitude) and 0.05 meters
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