  This paper explores the application of the GPT-4V(ision) large visual
language model to autonomous driving in mining environments, where traditional
systems often falter in understanding intentions and making accurate decisions
during emergencies. GPT-4V introduces capabilities for visual question
answering and complex scene comprehension, addressing challenges in these
specialized settings.Our evaluation focuses on its proficiency in scene
understanding, reasoning, and driving functions, with specific tests on its
ability to recognize and interpret elements such as pedestrians, various
vehicles, and traffic devices. While GPT-4V showed robust comprehension and
decision-making skills, it faced difficulties in accurately identifying
specific vehicle types and managing dynamic interactions. Despite these
challenges, its effective navigation and strategic decision-making demonstrate
its potential as a reliable agent for autonomous driving in the complex
conditions of mining environments, highlighting its adaptability and
operational viability in industrial settings.
