  Line-of-sight link blockages represent a key challenge for the reliability
and latency of millimeter wave (mmWave) and terahertz (THz) communication
networks. This paper proposes to leverage LiDAR sensory data to provide
awareness about the communication environment and proactively predict dynamic
link blockages before they happen. This allows the network to make proactive
decisions for hand-off/beam switching which enhances its reliability and
latency. We formulate the LiDAR-aided blockage prediction problem and present
the first real-world demonstration for LiDAR-aided blockage prediction in
mmWave systems. In particular, we construct a large-scale real-world dataset,
based on the DeepSense 6G structure, that comprises co-existing LiDAR and
mmWave communication measurements in outdoor vehicular scenarios. Then, we
develop an efficient LiDAR data denoising (static cluster removal) algorithm
and a machine learning model that proactively predicts dynamic link blockages.
Based on the real-world dataset, our LiDAR-aided approach is shown to achieve
95\% accuracy in predicting blockages happening within 100ms and more than 80\%
prediction accuracy for blockages happening within one second. If used for
proactive hand-off, the proposed solutions can potentially provide an order of
magnitude saving in the network latency, which highlights a promising direction
for addressing the blockage challenges in mmWave/sub-THz networks.
