Upload README.md

README.md

@@ -11,12 +11,11 @@ multimodal: true
 pretty_name: ScenePilot-4K
 ---
 
-
 # **ScenePilot-4K: A Large-Scale First-Person Dataset and Benchmark for Vision-Language Models in Autonomous Driving**
 
 <div align="center">
   <img src="assets/fig1.png" width="800px">
-  <p><em>Figure 1: Overview of the ScenePilot-Bench dataset and evaluation metrics.</em></p>
+  <p><em>Figure 1: Overview of the ScenePilot-Bench benchmark and evaluation metrics.</em></p>
 </div>
 
 <p align="center">
@@ -33,44 +32,53 @@ pretty_name: ScenePilot-4K
 
 ---
 
+## 📖 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 dataset files in this repository can be grouped into the following categories.
+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_2.5_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**
+- **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. Spatial Perception and Annotation Data
+## 2. Annotation and Perception Data
 
 - **VGGT.zip**  
-  Contains annotation data related to spatial perception tasks, including:
-  - Ego-vehicle trajectory information  
-  - Depth-related information  
+  Contains annotation data related to spatial perception and geometric reasoning, 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.
+  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  
+  - Weather conditions
+  - Road types
+  - Other environmental and semantic attributes
 
   These descriptions are used for scene understanding and for constructing balanced dataset splits.
 
@@ -81,9 +89,9 @@ Both models are trained using the same dataset and unified training pipeline, an
 - **split_train_test_val.zip**
 
 This file contains the **original video-level dataset split**, including:
-- Training set  
-- Validation set  
-- Test set  
+- 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.
 
@@ -95,15 +103,13 @@ All VQA datasets of different scales are constructed **strictly based on this vi
 
 - **All-VQA.zip**
 
-This archive contains all VQA data in JSON format.  
-Files are organized according to training, validation, and test splits.
+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.
+These files together form the complete training VQA dataset. Other files correspond to validation and test data.
 
 ---
 
@@ -138,16 +144,52 @@ Additional subsets include:
 
 ---
 
-## 5. Video Index and Download Information
+### 4.4 Spatial VQA
 
-- **video_name_all.xlsx**
+- **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.
+
+---
 
-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.
+### 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.
 
 ---
 
-## 🔧 Data Processing Utility
+## 5. Pipeline Code and Utilities
+
+- **pipeline_code.zip**
+
+This archive includes the primary code used in the annotation pipeline, covering:
+- Data preprocessing
+- Trajectory cleaning
+- VQA generation
+
+These scripts provide the main implementation resources for understanding and reproducing the core dataset construction workflow.
 
 - **clip.py**
 
@@ -159,24 +201,25 @@ The script performs the following operations:
 - Organizes extracted frames into structured folders  
 
 ---
+---
+
+## 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
+## 📚 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
-
-[![License](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
-
-This project is licensed under the Apache License 2.0 - see the [LICENSE](LICENSE) file for details.
+@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}, 
+}