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.

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📖 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.

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📦 Contents Overview

The released 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.

• 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.

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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.

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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

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.

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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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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.

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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.

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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.

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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.

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📚 Citation

@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}, 
}