Telkwevr/Bench2Drive-VL-base

Bench2Drive-VL: Full-Stack Software for Closed-Loop Autonomous Driving with Vision Language Models

Project Page | GitHub | Paper

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 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.

Citation

If you use this dataset in your research, please cite:

@article{Bench2DriveVL,
    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},
}