针对自动驾驶车辆通信瓶颈，提出自适应压缩算法提升数据融合效率。
Channel-Aware Throughput Maximization for Cooperative Data Fusion in CAV
- 用自监督编码器实现动态数据压缩，根据信道情况优化传输速率。
- 实验显示网络吞吐量提升超20.19%，平均精度提高9.38%，延迟仅19.99毫秒。
- 适合车联网、自动驾驶系统中需高效协同感知的场景使用。
联网与自动驾驶车辆（CAVs）因扩展感知范围和增强传感覆盖而受到广泛关注。为解决盲区与遮挡问题，CAVs利用车对车（V2V）通信聚合周围车辆的感知数据。然而，协同感知常受限于可实现的网络吞吐量与信道质量。本文提出一种信道感知的吞吐量最大化方法，通过自监督自动编码器实现自适应数据压缩。将问题建模为混合整数规划（MIP），分解为两个子问题以在给定链路条件下求解最优数据率与压缩比。随后训练自动编码器，在确定压缩比下最小化码率，并采用微调策略进一步降低频谱资源消耗。在OpenCOOD平台上的实验评估表明，所提算法相比现有最优方法，网络吞吐量提升超过20.19%，平均精度（AP@IoU）提高9.38%，最优延迟为19.99毫秒。
原文摘要 · Abstract (English)
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.
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