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.

翻译：多辆互联自动驾驶车辆间的协同感知通过通信交换补充信息，可显著提升感知能力。尽管已有方法取得进展，但协作车辆间的信道变化与数据异质性仍带来挑战。为此，我们提出ACC-DA——一种信道感知的协同感知框架，可动态调整通信图并最小化平均传输延迟，同时缓解数据异质性的负面影响。本文创新性体现在三个方面：首先设计传输延迟最小化方法，可根据不同信道状态信息构建通信图并最小化传输延迟；其次提出自适应数据重构机制，动态调节率失真权衡以提升感知效率，同时最小化数据传输中的时间冗余；最后构思域对齐方案，对齐不同车辆的数据分布，缓解车辆间的域差异并提升目标任务性能。综合实验表明，与现有最优方法相比，本方法具有显著有效性。