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.

翻译：近年来，自动驾驶因网联自动驾驶车辆（CAV）间的协同感知对提升道路安全的潜力而备受关注。然而，车辆传输环境中时变的信道变化要求动态分配通信资源。此外，在协同感知场景中，并非所有CAV都能提供有价值的数据，部分CAV数据甚至会对协同感知产生负面影响。本文提出SmartCooper——一种集成通信优化与判断器机制的自适应协同感知框架，以促进CAV数据融合。该方法首先在考虑通信约束的前提下优化车辆连接性，然后训练可学习编码器根据信道状态信息（CSI）动态调整压缩比，接着设计判断器机制以过滤自适应解码器重建的有害图像数据。我们在OpenCOOD平台上验证了所提算法的有效性。结果表明，与无判断器的方案相比，通信成本降低23.10%；同时，与现有最优方案相比，平均交并比精度（AP@IoU）提升7.15%。