Joint Task Offloading, Inference Optimization and UAV Trajectory Planning for Generative AI Empowered Intelligent Transportation Digital Twin

TL;DR

This paper presents a joint optimization framework for UAV task offloading, inference, and trajectory planning to enhance generative AI-powered digital twins in transportation, using a novel deep reinforcement learning algorithm.

Contribution

It introduces a new joint optimization model and a deep reinforcement learning algorithm (SU-HATD3) for efficient system utility maximization in GAI-enabled UAV-based digital twins.

Findings

The proposed algorithm outperforms baseline methods in system utility.

It achieves faster convergence and better adaptability to network dynamics.

Numerical results validate the effectiveness of the joint optimization approach.

Abstract

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.