  Situational awareness as a necessity in the connected and autonomous vehicles
(CAV) domain is the subject of a significant number of researches in recent
years. The driver's safety is directly dependent on the robustness,
reliability, and scalability of such systems. Cooperative mechanisms have
provided a solution to improve situational awareness by utilizing high speed
wireless vehicular networks. These mechanisms mitigate problems such as
occlusion and sensor range limitation. However, the network capacity is a
factor determining the maximum amount of information being shared among
cooperative entities. The notion of feature sharing, proposed in our previous
work, aims to address these challenges by maintaining a balance between
computation and communication load. In this work, we propose a mechanism to add
flexibility in adapting to communication channel capacity and a novel
decentralized shared data alignment method to further improve cooperative
object detection performance. The performance of the proposed framework is
verified through experiments on Volony dataset. The results confirm that our
proposed framework outperforms our previous cooperative object detection method
(FS-COD) in terms of average precision.
