Connected and autonomous vehicles (CAVs) have garnered significant attention due to their extended perception range and enhanced sensing coverage. To address challenges such as blind spots and obstructions, CAVs employ vehicle-to-vehicle (V2V) communications to aggregate sensory data from surrounding vehicles. However, cooperative perception is often constrained by the limitations of achievable network throughput and channel quality. In this paper, we propose a channel-aware throughput maximization approach to facilitate CAV data fusion, leveraging a self-supervised autoencoder for adaptive data compression. We formulate the problem as a mixed integer programming (MIP) model, which we decompose into two sub-problems to derive optimal data rate and compression ratio solutions under given link conditions. An autoencoder is then trained to minimize bitrate with the determined compression ratio, and a fine-tuning strategy is employed to further reduce spectrum resource consumption. Experimental evaluation on the OpenCOOD platform demonstrates the effectiveness of our proposed algorithm, showing more than 20.19\% improvement in network throughput and a 9.38\% increase in average precision (AP@IoU) compared to state-of-the-art methods, with an optimal latency of 19.99 ms.

本研究解决了连接和自主车辆（CAV）在感知盲区和遮挡问题下，合作感知受限于网络吞吐量和信道质量的挑战。提出了一种通道感知的吞吐量最大化方法，通过自监督自动编码器进行自适应数据压缩，并通过混合整数规划模型优化数据传输和压缩比，从而在OpenCOOD平台上验证了该算法的有效性，网络吞吐量提升超过20.19%，并且平均精度提高了9.38%。

面向合作数据融合的通道感知吞吐量最大化