  Collaborative perception among multiple connected and autonomous vehicles can
greatly enhance perceptive capabilities by allowing vehicles to exchange
supplementary information via communications. Despite advances in previous
approaches, challenges still remain due to channel variations and data
heterogeneity among collaborative vehicles. To address these issues, we propose
ACC-DA, a channel-aware collaborative perception framework to dynamically
adjust the communication graph and minimize the average transmission delay
while mitigating the side effects from the data heterogeneity. Our novelties
lie in three aspects. We first design a transmission delay minimization method,
which can construct the communication graph and minimize the transmission delay
according to different channel information state. We then propose an adaptive
data reconstruction mechanism, which can dynamically adjust the rate-distortion
trade-off to enhance perception efficiency. Moreover, it minimizes the temporal
redundancy during data transmissions. Finally, we conceive a domain alignment
scheme to align the data distribution from different vehicles, which can
mitigate the domain gap between different vehicles and improve the performance
of the target task. Comprehensive experiments demonstrate the effectiveness of
our method in comparison to the existing state-of-the-art works.
