LightEMMA: A Longitudinal Evaluation of Vision-Language Models for Autonomous Driving

Understand

Rapid advances in vision-language models (VLMs) have generated growing interest in their application to autonomous driving.

• A prevailing assumption is that successive VLM generations will continually improve driving performance and eventually outperform state-of-the-art methods.
• To systematically examine this assumption, we introduce LightEMMA, a longitudinal framework for evaluating the autonomous driving performance of VLMs.
• LightEMMA uses a lightweight, unified evaluation protocol that assesses each model's intrinsic driving capability without model-specific fine-tuning, architectural changes, or prompt engineering.