  As wireless communication technology progresses towards the sixth generation
(6G), high-frequency millimeter-wave (mmWave) communication has emerged as a
promising candidate for enabling vehicular networks. It offers high data rates
and low-latency communication. However, obstacles such as buildings, trees, and
other vehicles can cause signal attenuation and blockage, leading to
communication failures that can result in fatal accidents or traffic
congestion. Predicting blockages is crucial for ensuring reliable and efficient
communications. Furthermore, the advent of 6G technology is anticipated to
integrate advanced sensing capabilities, utilizing a variety of sensor types.
These sensors, ranging from traditional RF sensors to cameras and Lidar
sensors, are expected to provide access to rich multimodal data, thereby
enriching communication systems with a wealth of additional contextual
information. Leveraging this multimodal data becomes essential for making
precise network management decisions, including the crucial task of blockage
detection. In this paper, we propose a Deep Learning (DL)-based approach that
combines Convolutional Neural Networks (CNNs) and customized Vision
Transformers (ViTs) to effectively extract essential information from
multimodal data and predict blockages in vehicular networks. Our method
capitalizes on the synergistic strengths of CNNs and ViTs to extract features
from time-series multimodal data, which include images and beam vectors. To
capture temporal dependencies between the extracted features and the blockage
state at future time steps, we employ a Gated Recurrent Unit (GRU)-based
architecture. Our results show that the proposed approach achieves high
accuracy and outperforms state-of-the-art solutions, achieving more than $95\%$
accurate predictions.
