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.

本论文探讨了将 GPT-4V 视觉语言模型应用于矿山环境中的自动驾驶，它能够理解意图并在紧急情况下做出准确决策。通过视觉问答和复杂场景理解，GPT-4V 解决了这些特殊环境下的挑战，如识别和解释行人、各种车辆和交通设备等元素。尽管 GPT-4V 在准确识别特定车型和管理动态交互方面存在困难，但其有效的导航和策略决策展示了其在矿山环境复杂条件下作为可靠自动驾驶代理的潜力，凸显了其在工业环境中的适应性和可操作性。