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802.11p based V2X communication uses stochastic medium access control, whichcannot prevent broadcast packet collision, in particular during high channelload. Wireless congestion control has been designed to keep the channel load atan optimal point. However, vehicles' lack of precise and granular knowledgeabout true channel activity, in time and space, makes it impossible to fullyavoid packet collisions. In this paper, we propose a machine learning approachusing deep neural network for learning the vehicles' transmit patterns, and assuch predicting future channel activity in space and time. We evaluate theperformance of our proposal via simulation considering multiple safety-relatedV2X services involving heterogeneous transmit patterns. Our results show thatpredicting channel activity, and transmitting accordingly, reduces collisionsand significantly improves communication performance.

Deep Learning-aided Application Scheduler for Vehicular Safety Communication