  The blockage is a key challenge for millimeter wave communication systems,
since these systems mainly work on line-of-sight (LOS) links, and the blockage
can degrade the system performance significantly. It is recently found that
visual information, easily obtained by cameras, can be utilized to extract the
location and size information of the environmental objects, which can help to
infer the communication parameters, such as blockage status. In this paper, we
propose a novel vision-aided handover framework for UAV-assisted V2X system,
which leverages the images taken by cameras at the mobile station (MS) to
choose the direct link or UAV-assisted link to avoid blockage caused by the
vehicles on the road. We propose a deep reinforcement learning algorithm to
optimize the handover and UAV trajectory policy in order to improve the
long-term throughput. Simulations results demonstrate the effectiveness of
using visual information to deal with the blockage issues.
