Update dataset card with paper link, task categories and citation

README.md

@@ -1,21 +1,44 @@
 ---
-license: apache-2.0
-task_categories:
-- question-answering
 language:
 - en
-tags:
-- autonomous_driving
-pretty_name: Bench2Drive-VL-base1000
+license: apache-2.0
 size_categories:
 - 10M<n<100M
+task_categories:
+- image-text-to-text
+pretty_name: Bench2Drive-VL-base1000
+tags:
+- autonomous-driving
 ---
+
 # Bench2Drive-VL: Full-Stack Software for Closed-Loop Autonomous Driving with Vision Language Models
 
-[Github](https://huggingface.co/datasets/rethinklab/Bench2Drive) | [Website](https://thinklab-sjtu.github.io/Bench2Drive-VL/)
+[**Project Page**](https://thinklab-sjtu.github.io/Bench2Drive-VL/) | [**GitHub**](https://github.com/Thinklab-SJTU/Bench2Drive-VL) | [**Paper**](https://huggingface.co/papers/2604.01259)
+
+**Bench2Drive-VL** is a comprehensive closed-loop benchmark for Vision-Language Models in Autonomous Driving (VLM4AD). It extends the Bench2Drive benchmark by introducing closed-loop evaluation and the `DriveCommenter` expert model for automated annotation.
+
+This repository contains the natural language annotations for the [Bench2Drive-Base1000](https://huggingface.co/datasets/rethinklab/Bench2Drive) dataset. These annotations were generated by the expert model `DriveCommenter` and provide full-stack VQA pairs covering perception, prediction, planning, and behavior tasks across diverse driving situations in CARLA.
+
+## Key Features
+
+- **DriveCommenter**: A closed-loop generator that automatically generates diverse, behavior-grounded question-answer pairs for all driving situations in CARLA.
+- **Unified Protocol**: An interface that allows modern VLMs to be directly plugged into the Bench2Drive closed-loop environment for comparison.
+- **Full-Stack VQA**: Annotations covering low-level perception (objects, signs, lanes) and high-level reasoning for planning and behavior.
+
+## License
+
+All assets and code are under the Apache 2.0 license unless specified otherwise.
 
-This is a natural language annotation of [https://huggingface.co/datasets/rethinklab/Bench2Drive](Bench2Drive-Base1000) dataset, generated by expert model DriveCommenter. This dataset provides full-stack VQAs covering perception, prediction, planning and behaviour tasks with 50 different questions.
+## Citation
 
-## License and Citation
+If you use this dataset in your research, please cite:
 
-All assets and code are under the Apache 2.0 license unless specified otherwise.
+```bibtex
+@article{Bench2DriveSpeed,
+    title={Bench2Drive-VL: Benchmarks for Closed-Loop Autonomous Driving with Vision-Language Models}, 
+    author={Xiaosong Jia, Yuqian Shao, Zhenjie Yang, Qifeng Li, Zhiyuan Zhang, Junchi Yan},
+    year={2026},
+    eprint={2604.01259},
+    archivePrefix={arXiv},
+}
+```