融合多传感器数据，提前预测毫米波车联网信号遮挡。
Multi-Modal Sensor Fusion for Proactive Blockage Prediction in mmWave Vehicular Networks
- 用摄像头、雷达等多模态数据独立处理后加权融合。
- 提前1.5秒预测，准确率最高达97.2%，推理仅需95.7毫秒。
- 适合智能交通与车载通信系统研发人员参考。
运行在毫米波（mmWave）频段的车联网通信系统易受车辆、行人和基础设施等动态障碍物导致的信号遮挡影响。为应对这一挑战，我们提出一种基于基础设施到车辆（I2V）架构的主动遮挡预测框架，融合摄像头、GPS、LiDAR和雷达等多种传感器输入。该方法采用针对各模态的深度学习模型独立处理传感器流，并基于验证性能使用softmax加权集成策略进行输出融合。评估结果显示，在提前最多1.5秒的情况下，仅使用摄像头的模型达到最优单模态平衡，F1分数为97.1%，推理时间为89.8毫秒；摄像头+雷达配置进一步提升准确率至97.2% F1，推理时间95.7毫秒。结果表明，多模态感知在mmWave遮挡预测中兼具有效性和高效性，为动态环境下的主动无线通信提供了可行路径。
原文摘要 · Abstract (English)
Vehicular communication systems operating in the millimeter wave (mmWave) band are highly susceptible to signal blockage from dynamic obstacles such as vehicles, pedestrians, and infrastructure. To address this challenge, we propose a proactive blockage prediction framework that utilizes multi-modal sensing, including camera, GPS, LiDAR, and radar inputs in an infrastructure-to-vehicle (I2V) setting. This approach uses modality-specific deep learning models to process each sensor stream independently and fuses their outputs using a softmax-weighted ensemble strategy based on validation performance. Our evaluations, for up to 1.5s in advance, show that the camera-only model achieves the best standalone trade-off with an F1-score of 97.1% and an inference time of 89.8ms. A camera+radar configuration further improves accuracy to 97.2% F1 at 95.7ms. Our results display the effectiveness and efficiency of multi-modal sensing for mmWave blockage prediction and provide a pathway for proactive wireless communication in dynamic environments.
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