无人机协同生成式AI，实现交通数字孪生的高效低时延更新
Joint Task Offloading, Inference Optimization and UAV Trajectory Planning for Generative AI Empowered Intelligent Transportation Digital Twin
- 联合优化任务卸载、推理与飞行路径，提升系统性能
- 相比基线算法，系统效用提升显著，收敛更快
- 适合研究智能交通与边缘计算融合的学者参考
为实现智能交通数字孪生（ITDT），需调度无人机（UAV）处理路边传感器的感知数据。在此背景下，将扩散模型等生成式人工智能（GAI）技术部署于无人机，以将原始数据转化为高质量、高价值信息。因此，我们提出赋能生成式AI的ITDT。无人机在动态移动中执行一系列扩散模型推理（DMI）任务，其动态处理过程同时影响数字孪生更新的保真度与延迟。本文将该问题建模为系统效用最大化（SUM）的联合优化问题，涵盖DMI任务卸载、推理优化与无人机轨迹规划。针对网络动态性，将其建模为异构智能体马尔可夫决策过程，并提出基于序列更新的异构智能体双延迟深度确定性策略梯度（SU-HATD3）算法，可快速学习近优解。数值结果表明，所提算法在系统效用与收敛速度上均优于多个基线算法。
原文摘要 · Abstract (English)
To implement the intelligent transportation digital twin (ITDT), unmanned aerial vehicles (UAVs) are scheduled to process the sensing data from the roadside sensors. At this time, generative artificial intelligence (GAI) technologies such as diffusion models are deployed on the UAVs to transform the raw sensing data into the high-quality and valuable. Therefore, we propose the GAI-empowered ITDT. The dynamic processing of a set of diffusion model inference (DMI) tasks on the UAVs with dynamic mobility simultaneously influences the DT updating fidelity and delay. In this paper, we investigate a joint optimization problem of DMI task offloading, inference optimization and UAV trajectory planning as the system utility maximization (SUM) problem to address the fidelity-delay tradeoff for the GAI-empowered ITDT. To seek a solution to the problem under the network dynamics, we model the SUM problem as the heterogeneous-agent Markov decision process, and propose the sequential update-based heterogeneous-agent twin delayed deep deterministic policy gradient (SU-HATD3) algorithm, which can quickly learn a near-optimal solution. Numerical results demonstrate that compared with several baseline algorithms, the proposed algorithm has great advantages in improving the system utility and convergence rate.
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