Foundation Models for Mining 5.0: Challenges, Frameworks, and Opportunities

Abstract

In recent years, there has been a widespread interest in autonomous driving, with researchers primarily focusing on issues such as edge computing, constrained scenarios, and model generalization. ChatGPT is a large-scale natural language model based on the Generative Pre-trained Transformer algorithm. It has gained popularity worldwide and is commonly referred to as a "large model" or "foundation model" due to its high parameter count (over 100M). These models possess human-like understanding capabilities, enabling them to address complex challenges through self-learning and unifying multiple subtasks. While these models are primarily designed for natural language processing (NLP) tasks, their effectiveness has also been proven in the visual and multi-modal domains. Therefore, foundation models hold promise for contributing to the development of autonomous driving technology. Autonomous driving in mining areas is considered an edge scenario, and the reliable capabilities of foundation models make them highly beneficial in such complex and harsh working environments. This article aims to explore the characteristics of mining areas, the features of foundation models, as well as the framework and key technologies related to large models in mining. It provides technical guidance for the implementation of next-generation (mining 5.0) foundation models in unmanned mines.

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