mlsanityData to 14 Oct 2026

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

arXiv 2404.16356 ↗First posted 25 Apr 2024cs.NI cs.AI cs.LGcited by 6

On the map: Mixture-of-experts → MoE in reinforcement learning and robotics

Abstract (arXiv)

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.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Placed in MoE in reinforcement learning and robotics, one of 12 directions in Mixture-of-experts. Builds on 58 of its 123 references that are in the map. First posted on arXiv 25 Apr 2024.

Builds on

58 of 123 references.

References · 58

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38 more
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50 most cited of 58 All 58 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 4; 2026: 1.

6 papers.

  1. 2024A Survey on Large Language Models for Communication, Network, and Service Management: Application Insights, Challenges, and Future Directionscited by 25
  2. 2025Integrating LLMs with ITS: Recent Advances, Potentials, Challenges, and Future Directionscited by 23
  3. 2026Sparse Models, Sparse Safety: Unsafe Routes in Mixture-of-Experts LLMscited by 9
  4. 2025AGI Enabled Solutions For IoX Layers Bottlenecks In Cyber-Physical-Social-Thinking Spacecited by 0
  5. 2025DRIVE: Dynamic Rule Inference and Verified Evaluation for Constraint-Aware Autonomous Drivingcited by 0
  6. 2025Large Multimodal Models-Empowered Task-Oriented Autonomous Communications: Design Methodology and Implementation Challengescited by 0

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