Upload README.md

README.md

@@ -1,13 +1,37 @@
+---
+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**
+
 <div align="center">
   <img src="assets/The overall structure.png" width="800px">
-  <p>Figure 1: Overview of the ScenePilot-Bench dataset and evaluation metrics.</p>
+  <p><em>Figure 1: Overview of the ScenePilot-Bench dataset and evaluation metrics.</em></p>
 </div>
 
+<p align="center">
+  <a href="https://github.com/yjwangtj/ScenePilot-Bench">
+    <img src="https://img.shields.io/badge/Project-Website-blue?style=flat-square">
+  </a>
+  <a href="https://huggingface.co/datasets/larswangtj/ScenePilot-4K/tree/main">
+    <img src="https://img.shields.io/badge/Dataset-Download-green?style=flat-square">
+  </a>
+  <a href="#">
+    <img src="https://img.shields.io/badge/Paper-Arxiv-red?style=flat-square">
+  </a>
+</p>
 
-[![Project Page](https://img.shields.io/badge/Project-Website-blue?style=flat-square)](https://github.com/yjwangtj/ScenePilot-Bench)
-[![Dataset](https://img.shields.io/badge/Dataset-Download-green?style=flat-square)](https://huggingface.co/datasets/larswangtj/ScenePilot-4K/tree/main) 
-[![Paper](https://img.shields.io/badge/Paper-Arxiv-red?style=flat-square)](#)
+---
 
 ## 📦 Contents Overview
 
@@ -33,19 +57,19 @@ Both models are trained using the same dataset and unified training pipeline, an
 
 - **VGGT.zip**  
   Contains annotation data related to spatial perception tasks, including:
-  - Ego-vehicle trajectory information
+  - 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.
+  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
+  - Weather conditions  
+  - Road types  
   - Other environmental and semantic attributes  
 
   These descriptions are used for scene understanding and for constructing balanced dataset splits.
@@ -57,9 +81,9 @@ 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.
 
@@ -71,14 +95,15 @@ 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.
+This archive contains all VQA data in JSON format.  
 Files are organized according to training, validation, and test splits.
 
-For example:
+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.
 
 ---
 
@@ -117,7 +142,7 @@ Additional subsets include:
 
 - **video_name_all.xlsx**
 
-This file lists all videos used in the dataset along with their corresponding download links.
+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.
 
 ---
@@ -129,12 +154,14 @@ It is provided to support dataset reproduction and access to the original video
 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
+- 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
+## 📚Citation
 
 ```bibtex
 @article{scenepilot,