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GPT-4V Explorations: Mining Autonomous Driving

Zixuan Li

TL;DR

This paper examines the use of the GPT-4V (vision) model for autonomous driving in mining environments, where traditional systems struggle with intent understanding and emergency decision-making. It conducts a structured evaluation across three dimensions—scene understanding, reasoning, and driving actions—testing recognition of pedestrians, vehicles, ore piles, mechanical infrastructure, and traffic signs, as well as emergency reasoning and sequential driving tasks. The findings show GPT-4V achieves robust scene comprehension and strategic driving decisions but has notable weaknesses in precise vehicle-type identification, dynamic interaction interpretation, and trajectory tracking in complex, unstructured mining scenes. Overall, GPT-4V demonstrates significant potential for industrial autonomous driving with further improvements in object recognition accuracy, motion reasoning, and robust path/planning in dynamic mining environments.

Abstract

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.

GPT-4V Explorations: Mining Autonomous Driving

TL;DR

This paper examines the use of the GPT-4V (vision) model for autonomous driving in mining environments, where traditional systems struggle with intent understanding and emergency decision-making. It conducts a structured evaluation across three dimensions—scene understanding, reasoning, and driving actions—testing recognition of pedestrians, vehicles, ore piles, mechanical infrastructure, and traffic signs, as well as emergency reasoning and sequential driving tasks. The findings show GPT-4V achieves robust scene comprehension and strategic driving decisions but has notable weaknesses in precise vehicle-type identification, dynamic interaction interpretation, and trajectory tracking in complex, unstructured mining scenes. Overall, GPT-4V demonstrates significant potential for industrial autonomous driving with further improvements in object recognition accuracy, motion reasoning, and robust path/planning in dynamic mining environments.

Abstract

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.

Paper Structure

This paper contains 18 sections, 47 figures.

Table of Contents

  1. Introduction
  2. Scenario Understanding
  3. Understanding of Persons in Scenes
  4. Understanding of vehicles in Scenes
  5. Understanding of Rail Pile in Scenes
  6. Understanding of mechanical infrastructure in Scenes
  7. Understanding of traffic traffic signs in Scenes
  8. Reasoning
  9. Emergency and extreme events
  10. Temporal Sequences
  11. Act as A Driver
  12. U-turning
  13. Overtaking
  14. Pathfinding
  15. Parking
  16. ...and 3 more sections

Figures (47)

  • Figure 1: An illustration showing the integration of visual language models such as GPT-4V. This picture is generated by DALL·E 3.
  • Figure 2: Greenhighlights the right answer in understanding,Redhighlights the wrong answer in understanding,Yellow highlights the incompetence in performing the task.
  • Figure 3: Greenhighlights the right answer in understanding,Redhighlights the wrong answer in understanding,Yellow highlights the incompetence in performing the task.
  • Figure 4: Greenhighlights the right answer in understanding,Redhighlights the wrong answer in understanding,Yellow highlights the incompetence in performing the task.
  • Figure 5: Greenhighlights the right answer in understanding,Redhighlights the wrong answer in understanding,Yellow highlights the incompetence in performing the task.
  • ...and 42 more figures