[Paper Review] Generative AI-empowered Simulation for Autonomous Driving in Vehicular Mixed Reality Metaverses

Minrui Xu, Dusit Niyato+6 more|arXiv (Cornell University)|Feb 16, 2023

Traffic control and management4 citations

TL;DR

This paper proposes a generative AI-powered simulation framework for autonomous driving in vehicular mixed reality Metaverses, leveraging digital twins and auction-based resource allocation to synthesize diverse, conditioned driving datasets. The approach enhances simulation efficiency and robustness, achieving up to 150% higher social surplus compared to baseline methods.

ABSTRACT

In the vehicular mixed reality (MR) Metaverse, the distance between physical and virtual entities can be overcome by fusing the physical and virtual environments with multi-dimensional communications in autonomous driving systems. Assisted by digital twin (DT) technologies, connected autonomous vehicles (AVs), roadside units (RSU), and virtual simulators can maintain the vehicular MR Metaverse via digital simulations for sharing data and making driving decisions collaboratively. However, large-scale traffic and driving simulation via realistic data collection and fusion from the physical world for online prediction and offline training in autonomous driving systems are difficult and costly. In this paper, we propose an autonomous driving architecture, where generative AI is leveraged to synthesize unlimited conditioned traffic and driving data in simulations for improving driving safety and traffic efficiency. First, we propose a multi-task DT offloading model for the reliable execution of heterogeneous DT tasks with different requirements at RSUs. Then, based on the preferences of AV's DTs and collected realistic data, virtual simulators can synthesize unlimited conditioned driving and traffic datasets to further improve robustness. Finally, we propose a multi-task enhanced auction-based mechanism to provide fine-grained incentives for RSUs in providing resources for autonomous driving. The property analysis and experimental results demonstrate that the proposed mechanism and architecture are strategy-proof and effective, respectively.

Motivation & Objective

• Address the high cost and scalability limitations of collecting large-scale, real-world driving data for autonomous vehicle training.
• Enable collaborative simulation in vehicular mixed reality Metaverses by fusing physical and virtual environments through digital twin (DT) technologies.
• Improve driving safety and traffic efficiency by generating diverse, conditionally controlled traffic and driving datasets using generative AI.
• Design an incentive mechanism that fairly and efficiently allocates computing and communication resources among roadside units (RSUs) for DT task offloading.
• Ensure strategy-proofness and adverse-selection resistance in resource allocation to maintain trust and efficiency in decentralized simulation environments.

Proposed method

• Proposes a multi-task digital twin (DT) offloading model to manage heterogeneous DT tasks—such as simulation, decision-making, and monitoring—based on varying computing, communication, and deadline requirements at RSUs.
• Employs conditional generative AI models (e.g., TSDreambooth) to synthesize unlimited, high-quality, and condition-specific driving and traffic datasets based on real-time conditions and user preferences.
• Introduces a multi-task enhanced auction-based mechanism (MTEPViSA) that enables fine-grained incentives for RSUs by considering both online and offline submarkets, addressing asymmetric information.
• Uses a generative score to evaluate and prioritize AI model performance, enhancing the accuracy of synthesized data for downstream AV training.
• Applies property analysis to prove the mechanism is strategy-proof and adverse-selection free, ensuring truthful bidding and system stability.
• Employs experimental validation using real-world datasets and fine-tuned models to evaluate performance across different data generation preferences (e.g., background modification, recontextualization).

Figure 1: The MR Metaverse architecture of DT-assisted autonomous driving systems with traffic and driving simulations empowered by generative AI.

Experimental results

Research questions

• RQ1 How can generative AI be effectively leveraged to synthesize diverse, condition-specific driving and traffic datasets for autonomous vehicle training in a vehicular Metaverse?
• RQ2 What is the optimal mechanism for offloading heterogeneous digital twin tasks from autonomous vehicles to roadside units (RSUs) under varying resource and deadline constraints?
• RQ3 How can an incentive mechanism be designed to fairly and efficiently allocate RSU resources while ensuring strategy-proofness and resistance to adverse selection?
• RQ4 To what extent does generative AI-enhanced simulation improve social surplus and system efficiency compared to traditional simulation without AI?
• RQ5 How does the quality of synthesized data vary across different generation preferences (e.g., recontextualization vs. background modification), and how does this affect downstream model performance?

Key findings

• The proposed generative AI-empowered simulation framework increases social surplus by at least 150% compared to simulations without generative AI.
• The MTEPViSA auction mechanism improves surplus by more than 50% compared to the PViSA baseline, especially as the number of tasks and market size increase.
• The validation model achieved a local relative accuracy of 0.82 on the full generated dataset, 0.42 on the background modification dataset, and 0.85 on the recontextualization dataset, indicating varying performance based on data type.
• The mechanism effectively addresses asymmetric information in offline submarkets, outperforming PViSA, which ignores potential surplus in such markets.
• The experimental results show a similar trend to simulations, though with more uneven distribution due to lower data quality in real-world testing, confirming the framework’s robustness.
• The system demonstrates that synthesized datasets significantly improve AI model performance in AVs, though the improvement is not uniform across all data generation preferences.

Figure 2: The screenshots of implemented driving simulation testbed [ 20 ] with synthetic traffic signs generated by the proposed generative diffusion model, named TSDreamBooth.