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Integration of Mixture of Experts and Multimodal Generative AI in Internet of Vehicles: A Survey

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arxiv 2404.16356 v1 pith:YHKSLKP5 submitted 2024-04-25 cs.NI cs.AIcs.LG

classification cs.NIcs.AIcs.LG
keywords integrationenablegenerativevehiclescollaborativedistributedexpertsincluding
verification ladder T0 review T1 audit T2 compute T3 formal
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Generative AI (GAI) can enhance the cognitive, reasoning, and planning capabilities of intelligent modules in the Internet of Vehicles (IoV) by synthesizing augmented datasets, completing sensor data, and making sequential decisions. In addition, the mixture of experts (MoE) can enable the distributed and collaborative execution of AI models without performance degradation between connected vehicles. In this survey, we explore the integration of MoE and GAI to enable Artificial General Intelligence in IoV, which can enable the realization of full autonomy for IoV with minimal human supervision and applicability in a wide range of mobility scenarios, including environment monitoring, traffic management, and autonomous driving. In particular, we present the fundamentals of GAI, MoE, and their interplay applications in IoV. Furthermore, we discuss the potential integration of MoE and GAI in IoV, including distributed perception and monitoring, collaborative decision-making and planning, and generative modeling and simulation. Finally, we present several potential research directions for facilitating the integration.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. DRIVE: Dynamic Rule Inference and Verified Evaluation for Constraint-Aware Autonomous Driving

    cs.RO 2025-08 unverdicted novelty 5.0 of 10

    DRIVE uses exponential-family likelihoods to learn soft driving constraints from expert data and injects them into convex optimization, reporting 0.0% constraint violations on inD, highD, and RoundD.

  2. AGI Enabled Solutions For IoX Layers Bottlenecks In Cyber-Physical-Social-Thinking Space

    cs.NI 2025-06 conditional novelty 3.0 of 10

    A systematic review claims AGI can mitigate data overload, protocol heterogeneity, and identity explosion in IoX layers, but the supporting evidence consists mostly of narrower AI systems.

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