Connected and automated vehicles (CAVs) have become a transformative technology that can change our daily life. Currently, millimeter-wave (mmWave) bands are identified as the promising CAV connectivity solution. While it can provide high data rate, their realization faces many challenges such as high attenuation during mmWave signal propagation and mobility management. Existing solution has to initiate pilot signal to measure channel information, then apply signal processing to calculate the best narrow beam towards the receiver end to guarantee sufficient signal power. This process takes significant overhead and time, hence not suitable for vehicles. In this study, we propose an autonomous and low-cost testbed to collect extensive co-located mmWave signal and other sensors data such as LiDAR (Light Detection and Ranging), cameras, ultrasonic, etc, traditionally for ``automated'', to facilitate mmWave vehicular communications. Intuitively, these sensors can build a 3D map around the vehicle and signal propagation path can be estimated, eliminating iterative the process via pilot signals. This multimodal data fusion, together with AI, is expected to bring significant advances in ``connected'' research.

翻译：网联自动驾驶汽车（CAVs）已成为可能改变日常生活的变革性技术。当前，毫米波频段被视为最具前景的CAV连接方案。尽管毫米波能提供高数据传输速率，但其应用仍面临诸多挑战，例如毫米波信号传播过程中的高衰减与移动性管理。现有方案需通过导频信号测量信道信息，再运用信号处理技术计算面向接收端的最佳窄波束，以确保足够的信号功率。该过程需要大量开销与时间，因此不适用于车载场景。本研究提出一种自主且低成本的测试平台，用于收集同步毫米波信号与其他传感器（如LiDAR、相机、超声波等）数据——这些传感器传统上用于实现"自动化"——以促进毫米波车载通信。直观而言，这些传感器可构建车辆周围的3D地图，并估算信号传播路径，从而消除基于导频信号的迭代过程。这种多模态数据融合与人工智能相结合，有望为"网联化"研究带来显著突破。