Automated Evaluation of Large Vision-Language Models on Self-driving Corner Cases

• URL: http://arxiv.org/abs/2404.10595v5
• Date: Fri, 06 Dec 2024 01:54:59 GMT
• Title: Automated Evaluation of Large Vision-Language Models on Self-driving Corner Cases
• Authors: Kai Chen, Yanze Li, Wenhua Zhang, Yanxin Liu, Pengxiang Li, Ruiyuan Gao, Lanqing Hong, Meng Tian, Xinhai Zhao, Zhenguo Li, Dit-Yan Yeung, Huchuan Lu, Xu Jia,
• Abstract summary: We propose CODA-LM, the first benchmark for the automatic evaluation of LVLMs for self-driving corner cases. We show that using the text-only large language models as judges reveals even better alignment with human preferences than the LVLM judges. Our CODA-VLM performs comparably with GPT-4V, even surpassing GPT-4V by +21.42% on the regional perception task.
• Score: 102.05741859030951
• License:
• Abstract: Large Vision-Language Models (LVLMs) have received widespread attention for advancing the interpretable self-driving. Existing evaluations of LVLMs primarily focus on multi-faceted capabilities in natural circumstances, lacking automated and quantifiable assessment for self-driving, let alone the severe road corner cases. In this work, we propose CODA-LM, the very first benchmark for the automatic evaluation of LVLMs for self-driving corner cases. We adopt a hierarchical data structure and prompt powerful LVLMs to analyze complex driving scenes and generate high-quality pre-annotations for the human annotators, while for LVLM evaluation, we show that using the text-only large language models (LLMs) as judges reveals even better alignment with human preferences than the LVLM judges. Moreover, with our CODA-LM, we build CODA-VLM, a new driving LVLM surpassing all open-sourced counterparts on CODA-LM. Our CODA-VLM performs comparably with GPT-4V, even surpassing GPT-4V by +21.42% on the regional perception task. We hope CODA-LM can become the catalyst to promote interpretable self-driving empowered by LVLMs.

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