  Vehicle platooning, one of the advanced services supported by 5G NR-V2X,
improves traffic efficiency in the connected intelligent transportation systems
(C-ITSs). However, the packet delivery ratio of platoon communication,
especially in the out-of-coverage area, is significantly impacted by the random
selection algorithms employed in the current resource allocation scheme. In
this paper, we first analyze the collision probability via the random selection
algorithm adopted in the current standard. Subsequently, we then investigate
the deep reinforcement learning (DRL) algorithm that decreases the collision
probability by letting the agent (vehicle) learn from the communication
environment. Monte Carlo simulation is employed to verify the results obtained
in the analytical model and to compare the results between the two discussed
algorithms. Numerical results show that the proposed DRL algorithm outperforms
the random selection algorithm in terms of different vehicle density, which at
least lowering the collision probability by 73% and 45% in low and high vehicle
density respectively.
