  The recent advancements in communication and computational systems has led to
significant improvement of situational awareness in connected and autonomous
vehicles. Computationally efficient neural networks and high speed wireless
vehicular networks have been some of the main contributors to this improvement.
However, scalability and reliability issues caused by inherent limitations of
sensory and communication systems are still challenging problems. In this
paper, we aim to mitigate the effects of these limitations by introducing the
concept of feature sharing for cooperative object detection (FS-COD). In our
proposed approach, a better understanding of the environment is achieved by
sharing partially processed data between cooperative vehicles while maintaining
a balance between computation and communication load. This approach is
different from current methods of map sharing, or sharing of raw data which are
not scalable. The performance of the proposed approach is verified through
experiments on Volony dataset. It is shown that the proposed approach has
significant performance superiority over the conventional single-vehicle object
detection approaches.
