Embodied Artificial Intelligence-Enabled Internet of Vehicles: Challenges and Solutions

With the rapid advancement of artificial intelligence (AI) technology, the Internet of Vehicles (IoV) is becoming increasingly important in intelligent transportation systems (ITSs). At the same time, large language models (LLMs) and generative AI (GenAI) are gradually playing significant roles in the IoV. Multimodal LLMs (MLLMs) are models capable of integrating multiple modalities of information, enhancing the environmental perception capabilities of the IoV. GenAI can generate highly complex virtual driving scenarios, which are used to test and optimize intelligent driving algorithms, reducing the risks and costs of real-world road testing. Embodied AI can interact with the environment and make decisions, and can learn in the simulated environments that are provided by GenAI. To address issues like low correlation of multimodal data, high-uncertainty of driving environment, and insufficient intelligence in the IoV, this article proposes a perception and decision-making system empowered by embodied AI. First, we utilize a transformer to achieve multimodal data fusion integrated with vehicle-to-vehicle (V2V) data. Second, the MLLM analyzes the situation based on driving intentions, communication conditions, and the perceived environmental expressions. Finally, the MLLM makes the optimal decisions and adjustments based on the analysis. The experimental results show that the integration of V2V enables the IoV to more comprehensively perceive a complex driving environment. The MLLM can empower the IoV to make accurate decisions in high-uncertainty scenarios, significantly improving route completion rates.