---
license: apache-2.0
task_categories:
- visual-question-answering
tags:
- autonomous-driving
- vision-language
- multimodal
- benchmark
multimodal: true
pretty_name: ScenePilot-4K
---
# **ScenePilot-4K: A Large-Scale First-Person Dataset and Benchmark for Vision-Language Models in Autonomous Driving**
Figure 1: Overview of the ScenePilot-Bench benchmark and evaluation metrics.
---
## 📖 Introduction
ScenePilot-4K is a large-scale first-person driving dataset for safety-aware vision-language learning and evaluation in autonomous driving. Built from public online driving videos, ScenePilot-4K contains 3,847 hours of video and 27.7M front-view frames spanning 63 countries/regions and 1,210 cities. It jointly provides scene-level natural-language descriptions, risk assessment labels, key-participant annotations, ego trajectories, and camera parameters through a unified multi-stage annotation pipeline.
Building on this dataset, we establish **ScenePilot-Bench**, a standardized benchmark that evaluates vision-language models along four complementary axes: **scene understanding**, **spatial perception**, **motion planning**, and **GPT-based semantic alignment**. The benchmark includes fine-grained metrics and geographic generalization settings that expose model robustness under cross-region and cross-traffic domain shifts.
Baseline results on representative open-source and proprietary vision-language models show that current models remain competitive in high-level scene semantics but still exhibit substantial limitations in geometry-aware perception and planning-oriented reasoning.
---
## 📦 Contents Overview
The released 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.
- **ScenePilot_2.5_3b_200k_merged.zip** corresponds to **Qwen2.5-VL-3B**
- **ScenePilot_2_2b_200k_merged.zip** 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. Annotation and Perception Data
- **VGGT.zip**
Contains annotation data related to spatial perception and geometric reasoning, including:
- Ego-vehicle trajectory information
- Depth-related information
- Camera intrinsic and extrinsic parameters
This file is **not the raw output of VGGT**, but a **post-processed version after trajectory cleaning**.
Specifically, the annotation pipeline is as follows:
1. Raw trajectory, depth, and camera parameters are first generated using `VGGT.py` from `pipeline_code.zip`
2. The generated trajectories are then processed using `traj_clean.py` to remove physically implausible or noisy trajectories
The final annotations in this archive therefore correspond to **cleaned and quality-controlled trajectory data**, suitable for downstream tasks such as trajectory prediction and spatial reasoning.
- **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`
The VQA data in this archive is generated using the **original VQA generation pipeline** and includes a total of **22 VQA categories (Q1–Q22)**:
After initial generation, parts of the dataset were **refined and regenerated** due to:
- Data cleaning
- Format standardization
- Improved annotation consistency
To support flexible usage, we provide:
- `classify.py` (in `pipeline_code.zip`)
→ A utility script that allows users to:
- Classify VQA samples into categories
- Select specific subsets of interest
- Combine old and newly refined VQA samples
Therefore, this archive contains a **mixture of original and partially updated VQA data**, and users are encouraged to use the provided tools to construct task-specific subsets.
---
### 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.
---
### 4.4 Spatial VQA
- **spatial_vqa.zip**
This archive contains the updated VQA dataset with **explicitly grounded target objects**, focusing exclusively on **spatial perception tasks**.
It includes the following seven question categories:
- **Q1**
- **Q6**
- **Q10**
- **Q11**
- **Q20**
- **Q21**
- **Q22**
These samples are designed to support more precise evaluation and training for object-grounded spatial perception in autonomous driving scenarios.
---
### 4.5 Trajectory VQA
- **trajectory_vqa.zip**
This archive contains a curated set of **high-quality trajectory-related VQA samples** obtained after trajectory filtering and cleaning.
It covers the following five trajectory-centric categories:
- **Q15**
- **Q16**
- **Q17**
- **Q18**
- **Q19**
These samples are intended for motion planning and trajectory reasoning tasks, with improved annotation quality after trajectory validation and filtering.
---
## 5. Pipeline Code and Utilities
- **pipeline_code.zip**
This archive contains the full implementation of the dataset construction pipeline. The components cover data preprocessing, perception annotation, trajectory generation, VQA construction, and post-processing.
The main scripts are listed below:
- **clip.py**
Extracts image frames from raw videos:
- Removes fixed durations at the beginning and end
- Samples frames at a fixed rate
- Organizes outputs into structured directories
- **mask.py**
Generates image masks based on 2D bounding boxes:
- Takes YOLO detection results as input
- Produces masked images for each detected object
- Supports region-based grounding in VQA tasks
- **Old_vqa_Q1-19.py**
Original VQA generation script:
- Produces full set of **19 question categories (Q1–Q19)**
- Forms the initial version of the VQA dataset
- **Q1-6-10-11_new.py**
Updated VQA generation logic for selected categories:
- Focuses on Q1, Q6, Q10, Q11
- Replaces ambiguous object references with **explicit region-based grounding**
- Introduces **region-id representations** (e.g., `Region[0]`)
- Each region is associated with a precise mask
- **Q20-21-22_new.py**
Updated generation for additional spatial reasoning categories:
- Applies the same **region-id grounding strategy**
- Improves clarity and consistency in spatial relationship reasoning
- **scene_description.py**
Generates scene-level descriptions:
- Operates on the **4th frame of each clip**
- Produces structured descriptions including environment and context
- **VGGT.py**
Core perception annotation module:
- Generates ego trajectories
- Prouces depth informadtion
- Outputs camera intrinsic and extrinsic parameters
- **traj_clean.py**
Trajectory post-processing module:
- Filters out noisy or physically implausible trajectories
- Improves annotation quality for planning-related tasks
- **classify.py**
VQA classification and selection tool:
- Supports both **old and new VQA formats**
- Allows users to:
- Filter by question category
- Select task-specific subsets
- Construct customized training datasets
These scripts together define the **complete and reproducible pipeline** for building the ScenePilot-4K dataset, from raw video processing to structured multimodal annotations.
---
---
## 6. 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.
---
## 📚 Citation
```bibtex
@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 Li Zhang and Bingzhao Gao and Daxin Tian and Hong Chen},
year={2026},
eprint={2601.19582},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2601.19582},
}
