  Cooperative perception offers several benefits for enhancing the capabilities
of autonomous vehicles and improving road safety. Using roadside sensors in
addition to onboard sensors increases reliability and extends the sensor range.
External sensors offer higher situational awareness for automated vehicles and
prevent occlusions. We propose CoopDet3D, a cooperative multi-modal fusion
model, and TUMTraf-V2X, a perception dataset, for the cooperative 3D object
detection and tracking task. Our dataset contains 2,000 labeled point clouds
and 5,000 labeled images from five roadside and four onboard sensors. It
includes 30k 3D boxes with track IDs and precise GPS and IMU data. We labeled
eight categories and covered occlusion scenarios with challenging driving
maneuvers, like traffic violations, near-miss events, overtaking, and U-turns.
Through multiple experiments, we show that our CoopDet3D camera-LiDAR fusion
model achieves an increase of +14.36 3D mAP compared to a vehicle camera-LiDAR
fusion model. Finally, we make our dataset, model, labeling tool, and dev-kit
publicly available on our website:
https://tum-traffic-dataset.github.io/tumtraf-v2x.
