Cooperative Perception

Run an OpenCOOD model over data from CAVs and RSUs. Complete First Simulation first and keep CARLA running.

Prepare the Image and Model

The following PointPillar example uses opencda-coperception. From the CAVISE root on Linux/WSL:

./run.sh build opencda-coperception
./run.sh up opencda-coperception
docker exec -it opencda bash

Inside OpenCDA, set CARLA_HOST as described in Connect from OpenCDA. Run a bounded baseline:

python opencda.py -t 2cars_2rsu_coperception \
  --carla-host "$CARLA_HOST" --with-coperception \
  --model-id pointpillar-late-opv2v-30 --ticks 200 --save-vis

OpenCDA resolves the requested checkpoint before starting the simulation. Missing bundles are fetched into the sibling models directory. See Models and Runtime Assets for model IDs, custom directories, metadata validation, and offline use. The first run needs network access if the bundle is not cached.

Choose the Right Target

opencda-coperception includes OpenCOOD without its custom CUDA extensions. Use opencda-cuda for models needing those extensions, such as FPV-RCNN. Use the full opencda target when also enabling Artery. All these images use a CUDA runtime. A smaller target is not a CPU-only image. See Runtime Commands and Targets for the complete target table.

Inspect the Results

--save-vis saves rendered predictions. --save-npy saves prediction arrays. --show-video-vis opens live visualization and needs a display. Output locations and interpretation are covered in Recording and Results.

Once the baseline works, continue to AdvCP to compare a run with and without a perception attack.