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| Main Author: | |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2406.16817 |
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| _version_ | 1866929398056222720 |
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| author | Li, Zixuan |
| author_facet | Li, Zixuan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_16817 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | GPT-4V Explorations: Mining Autonomous Driving Li, Zixuan Computer Vision and Pattern Recognition 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. |
| title | GPT-4V Explorations: Mining Autonomous Driving |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2406.16817 |