larswangtj/ScenePilot-4K

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.

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📦 Contents Overview

The dataset files in this repository can be grouped into the following categories.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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🔧 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

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📚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.