AutoDriDM: An Explainable Benchmark for Decision-Making of Vision-Language Models in Autonomous Driving

• URL: http://arxiv.org/abs/2601.14702v1
• Date: Wed, 21 Jan 2026 06:29:09 GMT
• Title: AutoDriDM: An Explainable Benchmark for Decision-Making of Vision-Language Models in Autonomous Driving
• Authors: Zecong Tang, Zixu Wang, Yifei Wang, Weitong Lian, Tianjian Gao, Haoran Li, Tengju Ru, Lingyi Meng, Zhejun Cui, Yichen Zhu, Qi Kang, Kaixuan Wang, Yu Zhang,
• Abstract summary: We present AutoDriDM, a decision-centric, progressive benchmark with 6,650 questions across three dimensions - Object, Scene, and Decision.<n>We evaluate mainstream vision-language models to delineate the perception-to-decision capability boundary in autonomous driving.<n>We conduct explainability analyses of models' reasoning processes, identifying key failure modes such as logical reasoning errors.
• Score: 26.866150191410032
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Autonomous driving is a highly challenging domain that requires reliable perception and safe decision-making in complex scenarios. Recent vision-language models (VLMs) demonstrate reasoning and generalization abilities, opening new possibilities for autonomous driving; however, existing benchmarks and metrics overemphasize perceptual competence and fail to adequately assess decision-making processes. In this work, we present AutoDriDM, a decision-centric, progressive benchmark with 6,650 questions across three dimensions - Object, Scene, and Decision. We evaluate mainstream VLMs to delineate the perception-to-decision capability boundary in autonomous driving, and our correlation analysis reveals weak alignment between perception and decision-making performance. We further conduct explainability analyses of models' reasoning processes, identifying key failure modes such as logical reasoning errors, and introduce an analyzer model to automate large-scale annotation. AutoDriDM bridges the gap between perception-centered and decision-centered evaluation, providing guidance toward safer and more reliable VLMs for real-world autonomous driving.

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