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DriveNetSim: A simulation framework for mobility aware and learning based vehicular task offloading
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AI
DOI：10.1016/j.simpat.2026.103331.png)
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Abstract
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Autonomous Vehicles (AVs) environments require task execution under continuously changing mobility, communication, and infrastructure conditions. Runtime offloading decisions depend on communication continuity, queue dynamics, resource availability, and execution feasibility during vehicle movement. Existing vehicular simulators commonly model mobility, wireless networking, or task offloading as separate subsystems, which limits unified evaluation of mobility aware vehicular computing and Artificial Intelligence (AI) driven scheduling behavior. This paper presents DriveNetSim, an integrated simulation framework for mobility aware and AI driven vehicular task offloading. The framework combines mobility aware communication and queue aware multi tier offloading within an integrated simulation. DriveNetSim supports Vehicular Edge (VE), Base Station (BS), Collaborative Vehicular Execution (Collaboration), Metro Edge (ME), and Cloud Computing (CC) tiers under heterogeneous Ultra Reliable Low Latency Communications (URLLC), Massive Machine Type Communications (mMTC), and Enhanced Mobile Broadband (eMBB) workloads. The simulator supports runtime feasibility analysis, collaborative execution control, oracle aligned diagnostics, regret analysis, and AI compatible scheduling evaluation. The simulator records queue dynamics, latency progression, mobility transitions, resource utilization, and scheduling behavior through reproducible runtime diagnostics and structured evaluation outputs. Experimental case studies demonstrate unified evaluation of mobility, communication, computation, collaboration, and AI driven scheduling within a reproducible vehicular computing environment.
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