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

TL;DR

This survey reviews how integrating Mixture of Experts and Multimodal Generative AI can advance full autonomy and intelligent functionalities in the Internet of Vehicles, covering fundamentals, applications, and future research directions.

Contribution

It provides a comprehensive overview of the integration of MoE and GAI in IoV, highlighting potential applications and outlining future research avenues.

Findings

Integration of MoE and GAI enables distributed perception and decision-making in IoV.

Potential for achieving artificial general intelligence in autonomous vehicles.

Identification of key research challenges and future directions for IoV AI integration.

Abstract

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.