This paper explores the potential of using large language models (LLMs), such as GPT-5.2 and Gemini 3, in the transportation industry through applications in vehicle design, autonomous navigation, traffic control, and other areas. Special attention is given to augmented sampling generation and multimodal processing. Key issues discussed include safety certification, model transparency, and ethical considerations of their implementation. Purpose: to investigate the prospects for using AI agents based on LLMs in the transport industry. Results: this paper considers the use of augmented sampling generation and multimodal data processing, along with examples, including traffic light control using AI, simulation scenario generation and driver fatigue analysis. Theoretical Significance: this paper concludes that the synergy between AI and transportation will inevitably lead to increased safety and efficiency, and that LLMs will play a significant role in future intelligent adaptive transport systems.
Andrey Tyugashev (Wed,) studied this question.