Self-supervised Adaptive Weighting for Cooperative Perception in V2V
  Communications
Chenguang Liu Jianjun Chen Yunfei Chen Ryan Payton Michael Riley Shuang-Hua Yang
Perception of the driving environment is critical for collision avoidance and
route planning to ensure driving safety. Cooperative perception has been widely
studied as an effective approach to addressing the shortcomings of
single-vehicle perception. However, the practical limitations of
vehicle-to-vehicle (V2V) communications have not been adequately investigated.
In particular, current cooperative fusion models rely on supervised models and
do not address dynamic performance degradation caused by arbitrary channel
impairments. In this paper, a self-supervised adaptive weighting model is
proposed for intermediate fusion to mitigate the adverse effects of channel
distortion. The performance of cooperative perception is investigated in
different system settings. Rician fading and imperfect channel state
information (CSI) are also considered. Numerical results demonstrate that the
proposed adaptive weighting algorithm significantly outperforms the benchmarks
without weighting. Visualization examples validate that the proposed weighting
algorithm can flexibly adapt to various channel conditions. Moreover, the
adaptive weighting algorithm demonstrates good generalization to untrained
channels and test datasets from different domains.