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Generative AI-empowered Simulation for Autonomous Driving in Vehicular Mixed Reality Metaverses

Minrui Xu, Dusit Niyato, Junlong Chen, Hongliang Zhang, Jiawen Kang, Zehui Xiong, Shiwen Mao, Zhu Han

arXiv:2302.08418v1cs.AIcs.NI

TL;DR

Large-scale realistic driving-data collection is costly, and conventional simulation still requires substantial manual labeling. The paper combines heterogeneous-task DT offloading, conditioned generative simulation, and an RSU incentive auction; experiments report a surplus increase of at least 150% over simulations without generative AI, and the mechanism is reported as strategy-proof and adverse-selection free.

  • Problem

    Large-scale realistic driving-data collection is costly, while conventional simulation data requires substantial manual labeling.

    Source: §I · p. 1

  • Method

    The paper proposes a DT-assisted architecture that offloads heterogeneous AV tasks to RSUs, synthesizes conditioned simulation data with generative AI, and uses an enhanced auction to incentivize RSU resources.

    Source: §I · p. 2

  • Results

    Generative AI-empowered simulations increase surplus by at least 150% compared with simulations without generative AI, and the proposed mechanism is strategy-proof and adverse-selection free.

    Source: §I · p. 2, 1) Performance Evaluation under… · p. 12

  • Takeaways & Limitations

    The proposed architecture combines DT task execution, conditioned simulation, and RSU incentives to support autonomous-driving systems in the vehicular Metaverse.

    Source: §VI · p. 12

Abstract

from arXiv · show

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.

I. INTRODUCTION

Large-scale realistic data collection for autonomous-driving simulation is costly, and conventional simulation data requires extensive manual labeling. The paper proposes a DT-assisted architecture combining conditioned generative simulation, heterogeneous-task offloading to RSUs, and an incentive mechanism for RSU resources.

I. INTRODUCTION

  • AV sensing is limited by incomplete environmental coverage, including occlusions, while simulation, training, and synchronization demand substantial RSU computing and communication resources.The paper identifies these resource demands as motivation for multi-task incentive mechanisms that encourage RSUs to support autonomous-driving systems.Source: §I · pp. 1–2
  • Generative AI addresses the manual-labeling burden of conventional simulation by directly synthesizing labeled data, including datasets conditioned on road and weather conditions and user preferences.The paper also describes TSDreamBooth, a diffusion-based traffic-sign generator developed using DreamBooth fine-tuned on the BelgiumTS dataset.Source: §I · pp. 1–2, Figure 2 · p. 2
  • The architecture links AVs, RSUs, and virtual simulators, using DT information and physical sensing data to synthesize massive, conditioned traffic and driving data for online and offline simulations.The simulators use DT information such as location, historical trajectory, and user preferences, together with collected physical-world sensing data.Source: §I · p. 2
  • The proposed mechanism is strategy-proof and adverse-selection free, and experiments report a 150% increase in total social surplus.The introduction attributes the property analysis to the mechanism and reports the surplus increase for the proposed framework.Source: §I · p. 2
  • AVs offload heterogeneous DT tasks with differing deadlines to RSUs, and the proposed auction provides fine-grained allocations and prices for those tasks.The tasks have different requirements, and the mechanism is designed for heterogeneous tasks with various deadlines.Source: §I · p. 2

A. DT-assisted Autonomous Driving

  • Earlier DT-based autonomous-driving work considered single, homogeneous DT tasks, which the paper describes as incompatible with heterogeneous computation needs across changing vehicle states and driving environments.Source: A. DT-assisted Autonomous Driving · p. 3

B. Generative AI-empowered Autonomous Driving Simulation

The paper combines generative-AI driving simulation with connected-vehicle resource allocation, addressing the gap between physical-world resource mechanisms and virtual-world simulation platforms. Its system model links AVs, RSUs, and virtual simulators through online decision support and offline training.

B. Generative AI-empowered Autonomous Driving Simulation

  • Earlier platforms could generate diverse simulations but could not directly synthesize labeled driving data conditioned on specific requirements, motivating the proposed generative-diffusion architecture.The paper also identifies a gap between virtual and physical driving experiences and sensor data.Source: B. Generative AI-empowered Autonomous… · p. 3

C. Incentive Mechanisms in Connected Vehicular Networks

  • The proposed architecture combines diffusion-based conditioned traffic and driving data synthesis with a multi-task enhanced second-score auction for RSU resources.Prior work focused on physical-world allocation or virtual-world simulation, rather than their synergy.Source: C. Incentive Mechanisms in Connected… · p. 3

III. SYSTEM MODEL

  • The system model covers connected AVs, RSUs, virtual simulators, network and DT task models, generative-AI simulation, and RSU incentives.The model formulates incentives around social surplus from resources provisioned to support autonomous driving.Source: §III · p. 3

A. The Architecture of DT-assisted Autonomous Driving

  • RSUs execute AV digital-twin tasks while virtual simulators use available resources and time for offline training; traffic simulators support online decisions and driving simulators provide offline practice.Simulation results are returned to AVs for future use.Source: A. The Architecture of DT-assisted… · pp. 3–4

B. Network Model

  • The model assumes RSUs have adequate communication and computing resources, with uplink and downlink channels and CPU and GPU capacity for DT execution and simulation.AVs generate multiple DT tasks, including simulation, decision-making, and monitoring, with heterogeneous resource needs and deadlines.Source: A. The Architecture of DT-assisted… · p. 4

C. Multi-task Digital Twin Model

AVs offload multiple heterogeneous digital-twin tasks to RSUs for remote execution. The model accounts for task transmission and computation, with simulation services using remaining RSU resources.

C. Multi-task Digital Twin Model

1) DT Task Execution:

  • RSUs receive offloaded DT tasks and execute them using computing resources, with task execution required to meet each task’s deadline.The model assumes RSUs can allocate virtual machines capable of completing DT tasks within their required deadlines.Source: 1) DT Task Execution · p. 5
  • After DT execution, virtual simulators can use remaining RSU communication and computing resources to provide traffic and driving simulation services and return results to AVs.The task model distinguishes upload transmission latency from computation latency at the RSU.Source: 1) DT Task Execution · p. 5

1) Generative AI-empowered Simulation:

The simulation approach fine-tunes generative models on simulator data and uses AV preferences to synthesize conditioned driving experiences. The model accounts for stochastic output and schedules simulations alongside DT execution.

1) Generative AI-empowered Simulation:

  • Fine-tuning uses paired images and prompts, class-specific prior-preservation loss, and low- and high-resolution image pairs to produce varied subject instances while retaining small details.The approach applies the Prior-Preservation Loss to customize local traffic signs.Source: 1) Generative AI-empowered Simulation · p. 5

2) TSDreambooth:

  • TSDreamBooth is fine-tuned on traffic-sign datasets and uses local signs and AV preference prompts to generate diverse conditioned driving data beyond the hit preference caches.RSUs extract preference caches from AV digital twins, using equipment such as eye-tracking devices; validation models can flag unsatisfactory generated experiences.Source: 2) TSDreambooth · p. 5
  • TSDreamBooth outputs are stochastic, and simulator match quality can be difficult to observe immediately for offline driving simulation, creating asymmetric information among simulators.The model represents simulation quality with a generative score and relates match quality to generated simulations and hit preference caches.Source: 2) TSDreambooth · pp. 5–6

3) Simulation and Offline Training Model:

  • Driving simulations run in the background of DT-task processing, with combined processing latency constrained by the AV’s deadline; the model states that offline simulation can improve communication and computing resource utilization.The simulation model accounts for effective transmission and GPU computation latency.Source: 3) Simulation and Offline Training Model · p. 6

E. Problem Formulation

The formulation uses an online and offline resource market to maximize social surplus while accounting for task reliability, allocation, externalities, and asymmetric information.

E. Problem Formulation

  • The resource market has online and offline submarkets that incentivize RSUs to provide communication and computing resources for AV and simulator services.The formulation maps AV and simulator values to payments and allocation probabilities, under a risk-neutral participant assumption.Source: E. Problem Formulation · p. 6

D “ = ErUi,0gSIM

  • The offline submarket models simulation-platform use with cost-per-time payments because AVs access simulators for a limited time while driving.The formulation weights driving- and traffic-simulator surplus using the driving simulator’s relative bargaining power.Source: D “ ErUi,0gSIM · p. 6

D ` SSIM

  • Allocation constraints require each physical or virtual entity to be assigned to exactly one RSU, while pricing constraints ensure individual rationality.Other stated constraints enforce DT-task deadline reliability and limit hit preference caches to cache size.Source: D ` SSIM · p. 6
  • The formulation identifies correlated AV and simulator surpluses as a source of externalities and asymmetric information that can produce adverse selection and inefficient matching.AV value in the online submarket can affect simulator surplus in the offline submarket through common simulation valuation.Source: D ` SSIM · p. 7
  • Online-market participants may not know which virtual simulator will be assigned, so AVs prefer a prefixed execution-latency threshold before allocation.Different simulators can have different match qualities for different AVs, affecting online processing latency.Source: D ` SSIM · p. 7
  • Traffic-simulation responses can be observed immediately, unlike the training value of offline driving simulations, which cannot be measured immediately by simulators.The formulation therefore notes the need to account for correlation and asymmetric information in allocation and pricing.Source: D ` SSIM · p. 7

IV. MULTI-TASK ENHANCED MECHANISM DESIGN

The section presents MTEPViSA, a multi-task enhanced second-score auction for allocating and pricing heterogeneous digital-twin tasks across online and offline submarkets. Its design uses task scores, allocation rules, and payments to address externalities and asymmetric information.

IV. MULTI-TASK ENHANCED MECHANISM DESIGN

  • MTEPViSA combines a multi-dimensional auction with an enhanced second-price auction through bidding, scoring, allocation, and pricing components.The mechanism is based on EPViSA and addresses externalities in the online submarket and asymmetric information in the offline submarket.Source: §IV · p. 7

A. Designing the MTEPViSA Mechanism

  • Online AVs submit bids and preferred task deadlines, and the auctioneer selects the AV with the highest calculated score for online simulation services.The score uses task deadlines and a price-vector element; the marginal score represents the score increase as the number of executed tasks increases.Source: A. Designing the MTEPViSA Mechanism · pp. 7–8
  • For offline simulation, virtual simulators submit bids, while the auctioneer uses allocation probabilities and a price-scaling factor α ≥ 1 to set outcomes and payments.The section states that introducing the scaling factor can increase RSUs’ expected social surplus compared with the traditional second-price auction.Source: A. Designing the MTEPViSA Mechanism · p. 8
  • Under the efficient multi-task DT scoring rule and cost-per-time model, MTEPViSA is fully strategy-proof and adverse-selection-free.The section also says its allocation and pricing rules are effective and efficient when the efficient scoring rule exists and the price scaling factor is selected as specified.Source: A. Designing the MTEPViSA Mechanism · p. 8

B. Property Analysis

This section derives an optimal task-deadline bidding strategy for AVs and uses selected deadlines to define an efficient multi-task DT scoring rule. The rule guides online allocation decisions toward maximizing social surplus.

B. Property Analysis

  • The optimal deadline bidding strategy for each AV task is given by an argmax over the task deadline, valuation, and externalities.The proposition states that each AV can choose the optimal deadlines to maximize its expected payoff.Source: B. Property Analysis · p. 8
  • For any given multi-dimensional bid, an alternative bid can yield expected utility at least as high as the original bid by using the deadline determined through Eq. (12).The proof states that the alternative deadline preserves the score and allocation probability.Source: B. Property Analysis · p. 8
  • The auctioneer uses AV bids and chosen optimal deadlines to maintain an efficient scoring rule that can maximize social surplus and guide online allocation.The section then defines the efficient multi-task DT scoring rule.Source: B. Property Analysis · p. 8

D pMq ` SSIM

The property analysis establishes MTEPViSA’s incentive and adverse-selection properties under an efficient multi-task scoring rule and a cost-per-time simulation model. It also describes critical payments as the basis for strategy-proofness.

D pMq ` SSIM

  • MTEPViSA is fully strategy-proof and adverse-selection-free under the efficient multi-task DT scoring rule and cost-per-time model of simulations.The paper explains strategy-proofness through critical payment functions for traders in both submarkets.Source: D pMq ` SSIM · p. 8
  • In the online submarket, critical payments protect bidders against false-name bidding, and the analysis concludes that the submarket is free from adverse selection.The adverse-selection analysis considers external effects from the offline submarket while holding other bidding prices constant.Source: D pMq ` SSIM · p. 9
  • In the offline submarket, the mechanism is loser false-name proof, while the paper’s broader conclusion is that it is free from adverse selection.The analysis uses the critical payment function and keeps simulation qualities constant in its adverse-selection argument.Source: D pMq ` SSIM · p. 9
  • The paper concludes that AVs and virtual simulators cannot gain higher utility by manipulating bids, and that the mechanism’s social surplus remains efficient enough to avoid market failure.The authors attribute these properties to the scoring rule and price-scaling factors addressing externalities and asymmetric information.Source: D pMq ` SSIM · p. 9

V. EXPERIMENTAL RESULTS

The experimental section evaluates the DT-assisted movement prediction model, generative AI-empowered traffic and driving simulation, and the proposed mechanism under different market parameters and system settings.

V. EXPERIMENTAL RESULTS

  • The evaluation includes the DT-assisted movement prediction model.Its performance is assessed in Subsection V-A1.Source: §V · p. 9
  • The experiments assess the generative AI-empowered traffic and driving simulation model.This evaluation is assigned to Subsection V-B.Source: §V · p. 9
  • The proposed mechanism is evaluated under different market parameters and system settings.This evaluation is assigned to Subsection V-C.Source: §V · p. 9

A. Experimental Setups

The experiments combine an MR Metaverse simulation with DT-assisted trajectory prediction and traffic-sign synthesis. The setup also includes human participants in an NYC scene.

A. Experimental Setups

  • The default system contains 30 AVs, 30 virtual simulators, and one RSU, with specified uplink and downlink channel allocations.The RSU is assigned 20 MHz uplink for DT task uploading and 20 MHz downlink for AR recommendation streaming.Source: A. Experimental Setups · p. 9
  • The setup visualizes differences between real-world and DT-assisted predicted trajectories and includes synthesized signs with background-color changes and re-contextualization.The figure captions identify these evaluated outputs but do not report their measured outcomes.Source: Figure 5 · p. 10, Figure 6 · p. 10
  • The simulation uses a 3D model of New York City blocks, where participants viewed an autonomous car and highway advertisements in an HMD.Participants provided eye-tracking data and completed a survey about their interest in each simulation.Source: A. Experimental Setups · p. 10

1) Digital Twin-assisted Vehicular Movement Prediction:

The movement-prediction experiment uses AVs’ historical DT trajectories to predict future locations and simulate movement in virtual space. It evaluates model fit and prediction performance with MSE and R2.

1) Digital Twin-assisted Vehicular Movement Prediction:

  • Historical AV locations over the last P steps are input to an RSU AI model to predict movement over the next F steps for virtual simulation.The predicted future trajectory is represented as τ_pre_i.Source: 1) Digital Twin-assisted Vehicular… · p. 10
  • The model’s training loss is mean squared error, while running prediction performance is evaluated with R2, which equals 1 for perfectly correlated predicted and true movements.The passage defines the historical trajectory as the input and describes evaluation against true movement.Source: 1) Digital Twin-assisted Vehicular… · p. 10
  • The experiment uses four trajectory collections sampled from and an LSTM model, then uses prediction results to calculate virtual-space simulation accuracy.R2 is used to evaluate the movement-prediction model.Source: 1) Digital Twin-assisted Vehicular… · p. 10

B. Traffic Sign Synthesizing in Generative AI-empowered Simulation

Experiments assess traffic-sign synthesis and auction-based resource allocation. The reported results include higher surplus with generative AI and the proposed mechanism, while generated-dataset improvements vary by preference.

1) Performance Evaluation under Different System Parameters:

  • With one task, generative-AI simulations increase surplus by at least 150% versus simulations without generative AI, while MTEPViSA improves surplus by more than half versus PViSA.Surplus rises with market size, which the authors attribute to increased competition among traders; generative score also substantially affects surplus.Source: Figure 8 · p. 11, 1) Performance Evaluation under… · p. 12

2) Performance Evaluation under Different System Settings:

  • In Fig. 9, MTEPViSA doubles surplus versus PViSA; Fig. 10 shows similar surplus-growth trends across mechanisms, but uneven experimental results across generated datasets.The improvement in AV AI-model performance over original datasets also varies with the dataset generated from different preferences.Source: Figure 9 · p. 11, 2) Performance Evaluation under… · p. 12, Figure 10 · p. 12

B. Traffic Sign Synthesizing in Generative AI-empowered Simulation

  • TSDreamBooth is fine-tuned on BelgiumTS traffic signs; the validation model recognizes around 80% of its generated images, with local relative accuracy varying across datasets.Reported local relative accuracy is 0.82 for the whole generated dataset, 0.42 for background modification, and 0.85 for re-contextualization.Source: B. Traffic Sign Synthesizing in… · p. 11

C. Performance Evaluation of Auction-based Mechanisms

  • PViSA selects the highest-valuation AV in the online submarket while ignoring potential surplus in the offline submarket.The comparison is discussed alongside the proposed mechanism and EPViSA.Source: C. Performance Evaluation of… · p. 12

VI. CONCLUSION

  • The proposed mechanism is strategy-proof and adverse-selection free, and experiments report an approximately 150% increase in social surplus.The conclusion describes the architecture as combining multi-task DT offloading, generative AI data synthesis, and an enhanced auction mechanism.Source: §VI · p. 12

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