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---
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.
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