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In this work, we propose the use of hybrid offloading of computing taskssimultaneously to edge servers (vertical offloading) via LTE communication andto nearby cars (horizontal offloading) via V2V communication, in order toincrease the rate at which tasks are processed compared to local processing.Our main contribution is an optimized resource assignment and schedulingframework for hybrid offloading of computing tasks. The framework optimallyutilizes the computational resources in the edge and in the micro cloud, whiletaking into account communication constraints and task requirements. Whilecooperative perception is the primary use case of our framework, the frameworkis applicable to other cooperative vehicular applications with high computingdemand and significant transmission overhead. The framework is tested in asimulated environment built on top of car traces and communication ratesexported from the Veins vehicular networking simulator. We observe asignificant increase in the processing rate of cooperative perception sensorframes when hybrid offloading with optimized resource assignment is adopted.Furthermore, the processing rate increases with V2V connectivity as morecomputing tasks can be offloaded horizontally.

Hybrid Vehicular and Cloud Distributed Computing: A Case for Cooperative Perception