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The Internet of Vehicles (IoV) has become a fundamental platform for advancing Intelligent Transportation Systems (ITSs) and Intelligent Connected Vehicles (ICVs). However, the increasing volume of data generated by vehicle sensors and the computational demands of Artificial Intelligence (AI) algorithms present significant challenges for the platform. Edge Intelligence (EI), which brings intelligent computing and data processing closer to vehicles, has emerged as a potential solution. In this survey, we provide a comprehensive overview of Edge Intelligence for the Internet of Vehicles. We begin by discussing the motivations behind employing EI in the IoV for typical AI computations. To fully exploit the potential of EI in heterogeneous IoV environments, we present a layered vehicular EI architecture and discuss its benefits and challenges. Furthermore, we provide a taxonomy of EI approaches for vehicular networks, focusing on cooperative inference, distributed training, and collaborative sensing, in terms of their schemas and advanced frameworks. Finally, we explore emerging trends and research directions in this field, including vehicle-road-cloud integration, generative AI-driven IoV, and vehicular cyber-physical fusion. By offering insights into state-of-the-art techniques and trends, this survey aims to enable researchers to develop innovative solutions for transforming the intelligent IoV ecosystem.
Yan et al. (Mon,) studied this question.