  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.
