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arXiv · 2503.21505

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

Yue Li·Meng Tian·Zhenyu Lin·Jiangtong Zhu·Dechang Zhu·Haiqiang Liu·Zining Wang·Yueyi Zhang·Zhiwei Xiong·Xinhai Zhao
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Abstract

Existing benchmarks for Vision-Language Model (VLM) on autonomous driving (AD) primarily assess interpretability through open-form visual question answering (QA) within coarse-grained tasks, which remain insufficient to assess capabilities in complex driving scenarios. To this end, we introduce $\textbf{VLADBench}$, a challenging and fine-grained dataset featuring close-form QAs that progress from static foundational knowledge and elements to advanced reasoning for dynamic on-road situations. The elaborate $\textbf{VLADBench}$ spans 5 key domains: Traffic Knowledge Understanding, General Element Recognition, Traffic Graph Generation, Target Attribute Comprehension, and Ego Decision-Making and Planning. These domains are further broken down into 11 secondary aspects and 29 tertiary tasks for a granular evaluation. A thorough assessment of general and domain-specific (DS) VLMs on this benchmark reveals both their strengths and critical limitations in AD contexts. To further exploit the cognitive and reasoning interactions among the 5 domains for AD understanding, we start from a small-scale VLM and train the DS models on individual domain datasets (collected from 1.4M DS QAs across public sources). The experimental results demonstrate that the proposed benchmark provides a crucial step toward a more comprehensive assessment of VLMs in AD, paving the way for the development of more cognitively sophisticated and reasoning-capable AD systems.

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Yue Li, Meng Tian, Zhenyu Lin, Jiangtong Zhu, Dechang Zhu, Haiqiang Liu, Zining Wang, Yueyi Zhang, Zhiwei Xiong, Xinhai Zhao. 2025-03-27. Fine-Grained Evaluation of Large Vision-Language Models in Autonomous Driving. https://arxiv.org/abs/2503.21505

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