VehicleWorld

dataset_info:
features:
- name: id
dtype: string
- name: execute.py
dtype: string
- name: inits
dtype: string
- name: modules
dtype: string
- name: querys
dtype: string
- name: raw
dtype: string
- name: worlds.json
dtype: string
splits:
- name: train
num_bytes: 155973757
num_examples: 1291
download_size: 155973757
dataset_size: 155973757
---

# 📚 Introduction

**VehicleWorld** is the first comprehensive multi-device environment for intelligent vehicle interaction that accurately models the complex, interconnected systems in modern cockpits. This environment enables precise evaluation of agent behaviors by providing real-time state information during execution. This dataset is specifically designed to evaluate the capabilities of Large Language Models (LLMs) as in-car intelligent assistants in understanding and executing complex commands within realistic vehicle environments.

- **GitHub Repository**: [OpenMOSS/VehicleWorld](https://github.com/OpenMOSS/VehicleWorld)
- **Paper**: [VehicleWorld: A Highly Integrated Multi-Device Environment for Intelligent Vehicle Interaction (arXiv:2509.06736)](https://arxiv.org/abs/2509.06736)

# 🌟 Dataset Overview

Through a meticulous construction process and expert review, our final dataset contains **1,291 carefully crafted tasks**. These tasks are strategically categorized into four splits based on the primary types of devices they involve:

- **Multimedia**: Tasks involving entertainment systems, audio controls, and media interfaces
- **Touch Control**: Tasks requiring interaction with touchscreen displays and control panels
- **Car Control**: Tasks related to vehicle operation, driving functions, and mechanical systems
- **Light**: Tasks involving interior and exterior lighting systems

The core strength of this dataset lies in evaluating agent performance within a highly complex environment characterized by:

- 🔧 **30** comprehensive vehicle function modules
- 🛠️ **250** functional APIs covering all major vehicle systems
- 📊 **680** queryable state properties for complete environment awareness
- ⚡ **Real-time** state feedback and execution monitoring

# 🚀 Getting Started

Follow these steps to integrate this dataset with the VehicleWorld project environment:

## 1. Prerequisites

Clone the main VehicleWorld repository:

```bash
git clone https://github.com/OpenMOSS/VehicleWorld.git
cd VehicleWorld
```

## 2. Download the Dataset

Navigate to the database directory and clone the dataset:

```bash
cd vehicleworld/database
git clone https://huggingface.co/datasets/OpenMOSS/VehicleWorld
```

## 3. Convert Dataset Structure

Run the conversion script to organize the dataset into the required directory structure:

```bash
python hf_convert.py
```

After the script completes, the task files will be properly organized in `vehicleworld/database/tasks/`, ready for use with the project's evaluation pipeline.

# 📂 Dataset Structure

Each sample in the dataset contains the following components:

| Field Name | Description |
| :------------- | :------------------------------------------------------------------------------------------------------ |
| `id` | Globally unique task identifier used as the directory name |
| `execute.py` | Python script for generating the corresponding `worlds.json` file |
| `inits` | Python code snippet for initializing the initial environment state |
| `modules` | Relevant vehicle function modules involved in the current task |
| `querys` | Natural language command or question posed by the user to the in-car assistant |
| `raw` | Raw text describing the task scenario, generated by the Claude-3.7-Sonnet model |
| `worlds.json` | List of world state transitions during task execution |

# 📚 Citation

If you find VehicleWorld or this dataset useful in your research, please cite our paper:

```bibtex
@misc{yang2025vehicleworldhighlyintegratedmultidevice,
title={VehicleWorld: A Highly Integrated Multi-Device Environment for Intelligent Vehicle Interaction},
author={Jie Yang and Jiajun Chen and Zhangyue Yin and Shuo Chen and Yuxin Wang and Yiran Guo and Yuan Li and Yining Zheng and Xuanjing Huang and Xipeng Qiu},
year={2025},
eprint={2509.06736},
archivePrefix={arXiv},
primaryClass={cs.AI},
url={https://arxiv.org/abs/2509.06736}
}
```

dataset_info:
特征:
- 名称: id
数据类型: 字符串
- 名称: execute.py
数据类型: 字符串
- 名称: inits
数据类型: 字符串
- 名称: modules
数据类型: 字符串
- 名称: querys
数据类型: 字符串
- 名称: raw
数据类型: 字符串
- 名称: worlds.json
数据类型: 字符串
划分集:
- 名称: train
字节数: 155973757
样本数: 1291
下载大小: 155973757
数据集大小: 155973757
---

# 📚 简介

**VehicleWorld** 是首个精准建模现代座舱复杂互联系统的综合性多设备智能车载交互环境。该环境可在执行过程中提供实时状态信息，实现对智能体行为的精准评估。本数据集专为评估大语言模型（Large Language Model，LLM）作为车载智能助手，在真实车载环境中理解并执行复杂指令的能力而设计。

- **GitHub 仓库**: [OpenMOSS/VehicleWorld](https://github.com/OpenMOSS/VehicleWorld)
- **论文**: [VehicleWorld: A Highly Integrated Multi-Device Environment for Intelligent Vehicle Interaction (arXiv:2509.06736)](https://arxiv.org/abs/2509.06736)

# 🌟 数据集概览

经过严谨的构建流程与专家评审，本数据集最终包含**1291个精心设计的任务**。这些任务根据其所涉及的核心设备类型，被划分为四个划分集：
- **多媒体**: 涉及娱乐系统、音频控制与媒体界面的任务
- **触控控制**: 需要与触控显示屏及控制面板交互的任务
- **车辆控制**: 与车辆操作、驾驶功能及机械系统相关的任务
- **灯光**: 涉及车内与车外照明系统的任务

本数据集的核心价值在于在以下高度复杂的环境中评估智能体性能：
- 🔧 **30** 个完整的车辆功能模块
- 🛠️ **250** 个覆盖所有主要车载系统的功能API
- 📊 **680** 个可查询的状态属性，以实现完整的环境感知
- ⚡ **实时** 状态反馈与执行监控

# 🚀 快速开始

请按照以下步骤将本数据集与VehicleWorld项目环境集成：

## 1. 前置条件
克隆VehicleWorld主仓库：
bash
git clone https://github.com/OpenMOSS/VehicleWorld.git
cd VehicleWorld

## 2. 下载数据集
进入数据库目录并克隆本数据集：
bash
cd vehicleworld/database
git clone https://huggingface.co/datasets/OpenMOSS/VehicleWorld

## 3. 转换数据集结构
运行转换脚本，将数据集整理为项目所需的目录结构：
bash
python hf_convert.py

脚本执行完成后，任务文件将被妥善组织至`vehicleworld/database/tasks/`目录下，可直接用于项目的评估流程。

# 📂 数据集结构

本数据集的每个样本包含以下组件：

| 字段名称 | 说明 |
| :------------- | :------------------------------------------------------------------------------------------------------ |
| `id` | 全局唯一任务标识符，用作目录名称 |
| `execute.py` | 用于生成对应`worlds.json`文件的Python脚本 |
| `inits` | 用于初始化初始环境状态的Python代码片段 |
| `modules` | 当前任务涉及的相关车辆功能模块 |
| `querys` | 用户向车载智能助手提出的自然语言指令或问题 |
| `raw` | 由Claude-3.7-Sonnet模型生成的任务场景原始文本描述 |
| `worlds.json` | 任务执行过程中的世界状态转换列表 |

# 📚 引用

若您在研究中使用VehicleWorld或本数据集，请引用我们的论文：
bibtex
@misc{yang2025vehicleworldhighlyintegratedmultidevice,
title={VehicleWorld: A Highly Integrated Multi-Device Environment for Intelligent Vehicle Interaction},
author={Jie Yang and Jiajun Chen and Zhangyue Yin and Shuo Chen and Yuxin Wang and Yiran Guo and Yuan Li and Yining Zheng and Xuanjing Huang and Xipeng Qiu},
year={2025},
eprint={2509.06736},
archivePrefix={arXiv},
primaryClass={cs.AI},
url={https://arxiv.org/abs/2509.06736}
}