---
license: apache-2.0
task_categories:
- visual-question-answering
tags:
- autonomous-driving
- vision-language
- multimodal
- benchmark
multimodal: true
pretty_name: ScenePilot-Bench
---
# **ScenePilot-Bench: A Large-Scale First-Person Dataset and Benchmark for Evaluation of Vision-Language Models in Autonomous Driving**
Figure 1: Overview of the ScenePilot-Bench dataset and evaluation metrics.
---
## 📦 Contents Overview
The dataset files in this repository can be grouped into the following categories.
---
## 1. Model Weight Files
- **ScenePilot_2.5_3b_200k_merged.zip**
- **ScenePilot_2_2b_200k_merged.zip**
These two compressed files contain pretrained model weights obtained by training on a **200k-scale VQA training set** constructed in this work.
- The former corresponds to **Qwen2.5-VL-3B**
- The latter corresponds to **Qwen2-VL-2B**
Both models are trained using the same dataset and unified training pipeline, and are used in the main experiments and comparison studies.
---
## 2. Spatial Perception and Annotation Data
- **VGGT.zip**
Contains annotation data related to spatial perception tasks, including:
- Ego-vehicle trajectory information
- Depth-related information
These annotations are used to support experiments involving trajectory prediction and spatial understanding.
- **YOLO.zip**
Provides 2D object detection results for major traffic participants.
All detections are generated by a unified detection model and are used as perception inputs for downstream VQA and risk assessment tasks.
- **scene_description.zip**
Contains scene description results generated from the original data, including:
- Weather conditions
- Road types
- Other environmental and semantic attributes
These descriptions are used for scene understanding and for constructing balanced dataset splits.
---
## 3. Dataset Split Definition
- **split_train_test_val.zip**
This file contains the **original video-level dataset split**, including:
- Training set
- Validation set
- Test set
All VQA datasets of different scales are constructed **strictly based on this video-level split** to avoid scene-level information leakage.
---
## 4. VQA Datasets
### 4.1 All-VQA
- **All-VQA.zip**
This archive contains all VQA data in JSON format.
Files are organized according to training, validation, and test splits.
Examples include:
- `Deleted_2D_train_vqa_add_new.json`
- `Deleted_2D_train_vqa_new.json`
These files together form the complete training VQA dataset.
Other files correspond to validation and test data.
---
### 4.2 Test-VQA
- **Test-VQA.zip**
This archive contains the **100k-scale VQA test datasets** used in the experiments.
- `Deleted_2D_test_selected_vqa_100k_final.json`
Used as the main test set in the primary experiments.
Additional test sets are provided for generalization studies:
- Files ending with `europe`, `japan-and-korea`, `us`, and `other` correspond to geographic generalization experiments.
- Files ending with `left` correspond to left-hand traffic country experiments.
Each test set contains **100k VQA samples**.
---
### 4.3 Train-VQA
- **Train-VQA.zip**
This archive contains training datasets of different scales:
- **200k VQA**
- **2000k VQA**
Additional subsets include:
- Files ending with `china`, used for geographic generalization experiments.
- Files ending with `right`, used for right-hand traffic country experiments.
---
## 5. Video Index and Download Information
- **video_name_all.xlsx**
This file lists all videos used in the dataset along with their corresponding download links.
It is provided to support dataset reproduction and access to the original video resources.
---
## 🔧 Data Processing Utility
- **clip.py**
This repository provides a utility script for extracting image frames from raw videos.
The script performs the following operations:
- Trims a fixed duration from the beginning and end of each video
- Samples frames at a fixed rate
- Organizes extracted frames into structured folders
---
## 📚Citation
```bibtex
@article@misc{wang2026scenepilotbenchlargescaledatasetbenchmark,
title={ScenePilot-Bench: A Large-Scale Dataset and Benchmark for Evaluation of Vision-Language Models in Autonomous Driving},
author={Yujin Wang and Yutong Zheng and Wenxian Fan and Tianyi Wang and Hongqing Chu and Daxin Tian and Bingzhao Gao and Jianqiang Wang and Hong Chen},
year={2026},
eprint={2601.19582},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2601.19582},
}
```
## License
[](https://opensource.org/licenses/Apache-2.0)
This project is licensed under the Apache License 2.0 - see the [LICENSE](LICENSE) file for details.
