  The huge research interest in cellular vehicle-to-everything (C-V2X)
communications in recent days is attributed to their ability to schedule
multiple access more efficiently as compared to its predecessor technology,
i.e., dedicated short-range communications (DSRC). However, one of the foremost
issues still remaining is the need for the V2X to operate stably in a highly
dynamic environment. This paper proposes a way to exploit the dynamicity. That
is, we propose a resource allocation mechanism adaptive to the environment,
which can be an efficient solution for air interface congestion that a V2X
network often suffers from. Specifically, the proposed mechanism aims at
granting a higher chance of transmission to a vehicle with a higher crash risk.
As such, the channel access is prioritized to those with urgent needs. The
proposed framework is established based on reinforcement learning (RL), which
is modeled as a contextual multi-armed bandit (MAB). Importantly, the framework
is designed to operate at a vehicle autonomously without any assistance from a
central entity, which, henceforth, is expected to make a particular fit to
distributed V2X network such as C-V2X mode 4.
