GPT4V-AD-Exploration

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Basic Information

This repository is the official code and asset companion to a technical report titled "On the Road with GPT-4V(ision): Explorations of Utilizing Visual-Language Model as Autonomous Driving Agent." It collects original test images, case examples, and documented interactions where the GPT-4V visual-language model is evaluated on tasks relevant to autonomous driving. The README explains that the project explores scenario understanding, reasoning about driving scenes, and instances of the model serving as a driving agent. The repository is organized into categorized directories that group cases by task type and includes JSON files that capture the prompts and GPT-4V responses together with the PNG images the model analyzed. The repo is released under the MIT license and intended as a reproducible reference and dataset for researchers examining visual-language model behavior in driving contexts.

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App Details

Features
The repository provides a structured set of assets and annotations organized into clear categories: Scenario Understanding, Reasoning, and Act as A Driver (Serving as a Driving Agent). Each case folder pairs a .png image with a .json file that records the prompt used and the model"s responses, enabling inspection of inputs and outputs. The README highlights illustrative examples such as Weather Understanding, Corner Cases, and Driving Agent demonstrations to showcase different evaluation scenarios. The project includes a citation for the accompanying arXiv technical report and references related team projects. The codebase and assets are distributed under an MIT license and contributions are welcomed via issues or pull requests.
Use Cases
This repository is useful for researchers and developers who want concrete, reproducible examples of how a visual-language model like GPT-4V performs on autonomous driving tasks. By providing original images plus the exact prompts and recorded model responses, it supports qualitative analysis of perception, scene interpretation, and decision-making behaviors without requiring reimplementation of experiments. The categorized cases make it easier to find examples focused on weather, corner cases, reasoning chains, or multi-task driving scenarios. The assets and JSON records can be used for benchmarking, error analysis, demonstration, teaching, or as a starting dataset for further experiments. The accompanying citation and permissive license facilitate academic reuse and extension.

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