# GPT-4V Explorations: Mining Autonomous Driving

arXiv: https://arxiv.org/abs/2406.16817 · HTML: https://mlsanity.com/paper/2406.16817/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Driving VLMs and open-vocabulary detection, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 9 of its 14 references that are in the map.
- First posted on arXiv 24 Jun 2024.

## Placed in

- Multimodal LLMs / vision-language models / [Driving VLMs and open-vocabulary detection](https://mlsanity.com/mllm/driving-vlms-and-open-vocabulary-detection/)
- End-to-end autonomous driving / [VLMs for autonomous driving](https://mlsanity.com/driving/vlms-for-autonomous-driving/)

## Builds on (most cited first, 9 of 9)

- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [GLM: General Language Model Pretraining with Autoregressive Blank Infilling](https://mlsanity.com/paper/2103.10360/) (2021)
- [A Challenger to GPT-4V? Early Explorations of Gemini in Visual Expertise](https://mlsanity.com/paper/2312.12436/) (2023)
- [Exploring OCR Capabilities of GPT-4V(ision) : A Quantitative and In-depth Evaluation](https://mlsanity.com/paper/2310.16809/) (2023)
- [GPT4Vis: What Can GPT-4 Do for Zero-shot Visual Recognition?](https://mlsanity.com/paper/2311.15732/) (2023)
- [Remote Sensing ChatGPT: Solving Remote Sensing Tasks with ChatGPT and Visual Models](https://mlsanity.com/paper/2401.09083/) (2024)
- [An Early Evaluation of GPT-4V(ision)](https://mlsanity.com/paper/2310.16534/) (2023)
- [Gemini Pro Defeated by GPT-4V: Evidence from Education](https://mlsanity.com/paper/2401.08660/) (2023)
- [Scenario Engineering for Autonomous Transportation: A New Stage in Open-Pit Mines](https://mlsanity.com/paper/2405.15772/) (2024)

## Built on by (most cited first, 1 of 1)

- [PG-Attack: A Precision-Guided Adversarial Attack Framework Against Vision Foundation Models for Autonomous Driving](https://mlsanity.com/paper/2407.13111/) (2024)

## Abstract (arXiv)

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.