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README.md

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+<p align="right">English</p>
+
+<div align="center">
+
+# AutoDriDM: An Explainable Benchmark for Decision-Making of Vision-Language Models in Autonomous Driving
+
+**Paper (arXiv):** https://arxiv.org/abs/2601.14702  
+**Hugging Face Dataset:** https://huggingface.co/datasets/ColamentosZJU/AutoDriDM
+
+</div>
+
+AutoDriDM is a **decision-centric**, progressive benchmark for evaluating the **perception-to-decision** capability boundary of Vision-Language Models (VLMs) in autonomous driving.
+
+> **This release provides annotations only.**  
+> Please obtain the original images from the official sources (**nuScenes / KITTI / BDD100K**) and align them locally if you want to run image-based evaluation.
+
 ---
-license: apache-2.0
+
+## ✨ Overview
+
+### Key Facts
+
+- **Protocol:** 3 progressive levels — **Object → Scene → Decision**
+- **Tasks:** 6 tasks (two per level)
+- **Scale:** **6,650** QA items built from **1,295** front-facing images
+- **Risk-aware evaluation:** each item includes a 5-level risk label `danger_score ∈ {1,2,3,4,5}`  
+  - **High-risk** can be defined as `average danger_score ≥ 4.0`
+
 ---
+
+## 🧩 Benchmark Structure
+
+AutoDriDM follows a **progressive evaluation** protocol:
+
+- **Object Level:** identify key objects and recognize their states
+- **Scene Level:** understand global context (weather/illumination, special factors)
+- **Decision Level:** choose driving actions and assess risk levels
+
+---
+
+## 📦 Task List (6 JSON Files)
+
+The dataset contains **six tasks**, each provided as a JSON file:
+
+### Object Level (single-choice)
+
+- **Object-1 (`Object-1.json`)**: Identify the **key object** that most influences the driving decision.
+- **Object-2 (`Object-2.json`)**: Determine the **state** of a designated key object (e.g., traffic light state).
+
+### Scene Level (multiple-choice)
+
+- **Scene-1 (`Scene-1.json`)**: Recognize **weather / illumination** (e.g., daytime, nighttime, rain, snow, heavy fog).
+- **Scene-2 (`Scene-2.json`)**: Identify **special scene factors** that potentially affect driving decisions (e.g., accident scene, construction zone).
+
+### Decision Level (single-choice)
+
+- **Decision-1 (`Decision-1.json`)**: Select the **optimal driving action** for the ego vehicle.
+- **Decision-2 (`Decision-2.json`)**: Evaluate the **risk level** of a specified (potentially suboptimal) action.
+
+---
+
+## 🧾 Data Format (JSON)
+
+Each file is a JSON array. Each element is an object with the following fields:
+
+- `image_name` (string): image identifier/path  
+  - In this release, we provide annotations only; `image_name` is intended to be mapped to your local image storage.
+- `taskX_q` (string): question text for task X
+- `taskX_o` (string): option list as a single string (e.g., `"A....; B....; C...."`)
+- `taskX_a` (string): answer letters  
+  - **Single-choice tasks:** one letter (e.g., `"C"`)  
+  - **Multiple-choice tasks:** comma-separated letters (e.g., `"A,C"`)
+- `danger_score` (int or string): scenario risk label on a 5-level scale (**1=minimal**, **5=severe**)
+
+### Example (JSON)
+
+```json
+{
+  "image_name": "images/xxxx.jpg",
+  "task1_q": "...",
+  "task1_o": "A....; B....; C....",
+  "task1_a": "C",
+  "danger_score": "2"
+}
+```
+
+---
+
+## 🚀 How to Use
+
+### 1) Download Annotations
+
+Download the six JSON files from the Hugging Face dataset page:
+
+- https://huggingface.co/datasets/ColamentosZJU/AutoDriDM
+
+### 2) Load Annotations in Python
+
+```python
+import json
+
+with open("Object-1.json", "r", encoding="utf-8") as f:
+    data = json.load(f)
+
+print(len(data), list(data[0].keys()))
+```
+
+### 3) Local Image Alignment (for image-based evaluation)
+
+To evaluate with images, you must:
+
+1. Download the source datasets from the official providers:
+   - nuScenes
+   - KITTI
+   - BDD100K
+2. Prepare a local folder (example):
+   - `./images/`
+3. Map each `image_name` in JSON to an existing local file path in your environment.
+
+---
+
+## 📌 Citation
+
+If you use AutoDriDM in your research, please cite:
+
+```bibtex
+@article{tang2026autodridm,
+  title={AutoDriDM: An Explainable Benchmark for Decision-Making of Vision-Language Models in Autonomous Driving},
+  author={Tang, Zecong and Wang, Zixu and Wang, Yifei and Lian, Weitong and Gao, Tianjian and Li, Haoran and Ru, Tengju and Meng, Lingyi and Cui, Zhejun and Zhu, Yichen and others},
+  journal={arXiv preprint arXiv:2601.14702},
+  year={2026}
+}
+```
+
+---
+
+## ⚖️ License
+
+This project is released under the **Apache License 2.0**.  
+Some components or third-party implementations may be distributed under different licenses.
+
+---
+
+## 🙏 Acknowledgments
+
+We thank the open-source community and dataset providers (**nuScenes, KITTI, BDD100K**) that make this benchmark possible.
+