  Given its ability to control and manipulate wireless environments,
reconfigurable intelligent surface (RIS), also known as intelligent reflecting
surface (IRS), has emerged as a key enabler technology for the six-generation
(6G) cellular networks. In the meantime, vehicular environment radio
propagation is negatively influenced by a large set of objects that cause
transmission distortion such as high buildings. Therefore, this work is devoted
to explore the area of RIS technology integration with vehicular communications
while considering the dynamic nature of such communication environment.
Specifically, we provide a system model where RoadSide Unit (RSU) leverages RIS
to provide indirect wireless transmissions to disconnected areas, known as dark
zones. Dark zones are spots within RSU coverage where the communication links
are blocked due to the existence of blockages. In details, a discrete RIS is
utilized to provide communication links between the RSU and the vehicles
passing through out-of-service zones. Therefore, the joint problem of RSU
resource scheduling and RIS passive beamforming or phase-shift matrix is
formulated as an optimization problem with the objective of maximizing the
minimum average bit rate. The formulated problem is mixed integer non-convex
program which is difficult to be solved and does not account for the uncertain
dynamic environment in vehicular networks. Thereby, we resort to alternative
methods based on Deep Reinforcement Learning to determine RSU wireless
scheduling and Block Coordinate Descent (BCD) to solve for the phase-shift
matrix, \textit{i.e.,} passive beamforming, of the RIS. The Markov Decision
Process (MDP) is defined and the complexity of the solution approach is
discussed. Our numerical results demonstrate the superiority of our proposed
approach over baseline techniques.
