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，自动驾驶技术因其通过互联自动驾驶车辆间的协同感知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通信资源的动态分配。此外，在协同感知场景中，并非所有互联自动驾驶车辆都能提供有效数据，部分车辆的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评判器机制的自适应协同感知框架，用于促进互联自动驾驶车辆的数据融合。我们的方法首先在考虑通信约束的条件下优化车辆连通性，随后训练可学习编码器根据信道状态信息动态调整压缩比，进而设计评判器机制以过滤由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。实验结果表明，与无评判器方案相比，通信成本降低了23.10%；同时相较于现有先进方案，平均交并比精度提升了7.15%。