In recent years, autonomous driving has garnered significant attention due to
its potential for improving road safety through collaborative perception among
connected and autonomous vehicles (CAVs). However, time-varying channel
variations in vehicular transmission environments demand dynamic allocation of
communication resources. Moreover, in the context of collaborative perception,
it is important to recognize that not all CAVs contribute valuable data, and
some CAV data even have detrimental effects on collaborative perception. In
this paper, we introduce SmartCooper, an adaptive collaborative perception
framework that incorporates communication optimization and a judger mechanism
to facilitate CAV data fusion. Our approach begins with optimizing the
connectivity of vehicles while considering communication constraints. We then
train a learnable encoder to dynamically adjust the compression ratio based on
the channel state information (CSI). Subsequently, we devise a judger mechanism
to filter the detrimental image data reconstructed by adaptive decoders. We
evaluate the effectiveness of our proposed algorithm on the OpenCOOD platform.
Our results demonstrate a substantial reduction in communication costs by
23.10\% compared to the non-judger scheme. Additionally, we achieve a
significant improvement on the average precision of Intersection over Union
(AP@IoU) by 7.15\% compared with state-of-the-art schemes.

SmartCooper 是一个自适应协同感知框架，结合了通信优化和判决机制，以提高车辆之间的连接性和共享感知数据的质量。在 OpenCOOD 平台上的评估结果显示，相比非判决方案，我们的算法成功降低了 23.10% 的通信成本，并将 Intersection over Union (IoU) 的平均精度 (AP@IoU) 提高了 7.15%。