  Cooperative perception, which has a broader perception field than
single-vehicle perception, has played an increasingly important role in
autonomous driving to conduct 3D object detection. Through vehicle-to-vehicle
(V2V) communication technology, various connected automated vehicles (CAVs) can
share their sensory information (LiDAR point clouds) for cooperative
perception. We employ an importance map to extract significant semantic
information and propose a novel cooperative perception semantic communication
scheme with intermediate fusion. Meanwhile, our proposed architecture can be
extended to the challenging time-varying multipath fading channel. To alleviate
the distortion caused by the time-varying multipath fading, we adopt explicit
orthogonal frequency-division multiplexing (OFDM) blocks combined with channel
estimation and channel equalization. Simulation results demonstrate that our
proposed model outperforms the traditional separate source-channel coding over
various channel models. Moreover, a robustness study indicates that only part
of semantic information is key to cooperative perception. Although our proposed
model has only been trained over one specific channel, it has the ability to
learn robust coded representations of semantic information that remain
resilient to various channel models, demonstrating its generality and
robustness.
