---
pretty_name: AutoDriDM
license: apache-2.0
language:
  - en
task_categories:
  - question-answering
tags:
  - autonomous-driving
  - vision-language-models
  - vlm
  - benchmark
  - explainability
size_categories:
  - 1K<n<10K
configs:
  - config_name: default
    data_files:
      - split: test
        path:
          - Object-1.json
          - Object-2.json
          - Scene-1.json
          - Scene-2.json
          - Decision-1.json
          - Decision-2.json
---

<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.

---

## ✨ 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.