VLMs Guided Interpretable Decision Making for Autonomous Driving

• URL: http://arxiv.org/abs/2511.13881v1
• Date: Mon, 17 Nov 2025 19:57:51 GMT
• Title: VLMs Guided Interpretable Decision Making for Autonomous Driving
• Authors: Xin Hu, Taotao Jing, Renran Tian, Zhengming Ding,
• Abstract summary: We evaluate state-of-the-art open-source vision-language models (VLMs) on high-level decision-making tasks.<n>We propose a new approach that shifts the role of VLMs from direct decision generators to semantic enhancers.<n>Our approach achieves state-of-the-art performance, offering a promising direction for integrating VLMs into reliable and interpretable AD systems.
• Score: 39.29020915361483
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advancements in autonomous driving (AD) have explored the use of vision-language models (VLMs) within visual question answering (VQA) frameworks for direct driving decision-making. However, these approaches often depend on handcrafted prompts and suffer from inconsistent performance, limiting their robustness and generalization in real-world scenarios. In this work, we evaluate state-of-the-art open-source VLMs on high-level decision-making tasks using ego-view visual inputs and identify critical limitations in their ability to deliver reliable, context-aware decisions. Motivated by these observations, we propose a new approach that shifts the role of VLMs from direct decision generators to semantic enhancers. Specifically, we leverage their strong general scene understanding to enrich existing vision-based benchmarks with structured, linguistically rich scene descriptions. Building on this enriched representation, we introduce a multi-modal interactive architecture that fuses visual and linguistic features for more accurate decision-making and interpretable textual explanations. Furthermore, we design a post-hoc refinement module that utilizes VLMs to enhance prediction reliability. Extensive experiments on two autonomous driving benchmarks demonstrate that our approach achieves state-of-the-art performance, offering a promising direction for integrating VLMs into reliable and interpretable AD systems.

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