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To reduce the amount of transmitted data, feature map based fusion isrecently proposed as a practical solution to cooperative 3D object detection byautonomous vehicles. The precision of object detection, however, may requiresignificant improvement, especially for objects that are far away or occluded.To address this critical issue for the safety of autonomous vehicles and humanbeings, we propose a cooperative spatial feature fusion (CoFF) method forautonomous vehicles to effectively fuse feature maps for achieving a higher 3Dobject detection performance. Specially, CoFF differentiates weights amongfeature maps for a more guided fusion, based on how much new semanticinformation is provided by the received feature maps. It also enhances theinconspicuous features corresponding to far/occluded objects to improve theirdetection precision. Experimental results show that CoFF achieves a significantimprovement in terms of both detection precision and effective detection rangefor autonomous vehicles, compared to previous feature fusion solutions.

CoFF: Cooperative Spatial Feature Fusion for 3D Object Detection on Autonomous Vehicles