Datasets:

larswangtj
/

ScenePilot-4K

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

@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

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.