VLADBench introduces a rigorous evaluation framework for testing how well large vision-language models understand and reason about complex driving scenarios.

• Moves beyond simple question-answering to multi-level assessment from basic knowledge to advanced reasoning
• Provides fine-grained metrics specifically designed for autonomous driving challenges
• Exposes critical performance gaps in current vision-language models when applied to driving contexts

This research is essential for engineering safer autonomous vehicles by establishing more comprehensive testing standards before deployment in real-world environments where safety is paramount.

Fine-Grained Evaluation of Large Vision-Language Models in Autonomous Driving