  802.11p based V2X communication uses stochastic medium access control, which
cannot prevent broadcast packet collision, in particular during high channel
load. Wireless congestion control has been designed to keep the channel load at
an optimal point. However, vehicles' lack of precise and granular knowledge
about true channel activity, in time and space, makes it impossible to fully
avoid packet collisions. In this paper, we propose a machine learning approach
using deep neural network for learning the vehicles' transmit patterns, and as
such predicting future channel activity in space and time. We evaluate the
performance of our proposal via simulation considering multiple safety-related
V2X services involving heterogeneous transmit patterns. Our results show that
predicting channel activity, and transmitting accordingly, reduces collisions
and significantly improves communication performance.
