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Trajectory-aware cooperative task mapping in vehicular edge computing via multi-agent reinforcement learning
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DOI：10.1016/j.future.2026.108755.png)
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Abstract
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Vehicular Edge Computing (VEC) brings cloud resources to the Road Side Units (RSUs) and the vehicles to support computation-intensive services in the Internet of Vehicles. The key challenge is allocating and scheduling dependent tasks across mobile vehicles and edge servers under tight time and energy budgets. We propose a trajectory-aware task mapping framework to coordinate vehicles and RSUs for task processing via Vehicle-to-Infrastructure (V2I) and Vehicle-to-Vehicle (V2V) communications. The applications generated by the vehicles can be modeled as Directed Acyclic Graphs (DAGs), and we jointly optimize task-to-device mapping subject to time and energy constraints. First, we design a Space-Time Long Short-Term Memory (ST-LSTM) model to predict short-term vehicle trajectories and contact durations, thereby enabling the selection of suitable vehicles for V2V task migration. Then, we use a task-priority sorting algorithm to schedule the tasks in the DAG so that all dependent tasks are executed in sequence. Finally, we develop MAIAC, a multi-agent actor-critic method that runs in a distributed manner and uses convolutional and pooling layers to learn spatial and temporal features for practical training. Experimental results show that ST-LSTM reduces trajectory error by more than 10% compared with a standard LSTM model. MAIAC converges faster, achieving lower task latency of up to 54% and reducing energy consumption by up to 50% compared with the state-of-the-art methods. The results demonstrate that combining mobility prediction with multi-agent reinforcement learning enables efficient and reliable task mapping in the VEC scenario.
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