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Autonomous Vehicles (AVs) use multiple sensors to gather information abouttheir surroundings. By sharing sensor data between Connected AutonomousVehicles (CAVs), the safety and reliability of these vehicles can be improvedthrough a concept known as cooperative perception. However, recent approachesin cooperative perception only share single sensor information such as camerasor LiDAR. In this research, we explore the fusion of multiple sensor datasources and present a framework, called CoBEVFusion, that fuses LiDAR andcamera data to create a Bird's-Eye View (BEV) representation. The CAVs processthe multi-modal data locally and utilize a Dual Window-based Cross-Attention(DWCA) module to fuse the LiDAR and camera features into a unified BEVrepresentation. The fused BEV feature maps are shared among the CAVs, and a 3DConvolutional Neural Network is applied to aggregate the features from theCAVs. Our CoBEVFusion framework was evaluated on the cooperative perceptiondataset OPV2V for two perception tasks: BEV semantic segmentation and 3D objectdetection. The results show that our DWCA LiDAR-camera fusion model outperformsperception models with single-modal data and state-of-the-art BEV fusionmodels. Our overall cooperative perception architecture, CoBEVFusion, alsoachieves comparable performance with other cooperative perception models.

CoBEVFusion: Cooperative Perception with LiDAR-Camera Bird's-Eye View Fusion