DriveXQA: Cross-modal Visual Question Answering for Adverse Driving Scene Understanding

Mingzhe Tao; Ruiping Liu; Junwei Zheng; Yufan Chen; Kedi Ying; M. Saquib Sarfraz; Kailun Yang; Jiaming Zhang; Rainer Stiefelhagen

arXiv · 2603.11380

DriveXQA: Cross-modal Visual Question Answering for Adverse Driving Scene Understanding

Mingzhe Tao·Ruiping Liu·Junwei Zheng·Yufan Chen·Kedi Ying·M. Saquib Sarfraz·Kailun Yang·Jiaming Zhang·Rainer Stiefelhagen

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Abstract

Fusing sensors with complementary modalities is crucial for maintaining a stable and comprehensive understanding of abnormal driving scenes. However, Multimodal Large Language Models (MLLMs) are underexplored for leveraging multi-sensor information to understand adverse driving scenarios in autonomous vehicles. To address this gap, we propose the DriveXQA, a multimodal dataset for autonomous driving VQA. In addition to four visual modalities, five sensor failure cases, and five weather conditions, it includes $102,505$ QA pairs categorized into three types: global scene level, allocentric level, and ego-vehicle centric level. Since no existing MLLM framework adopts multiple complementary visual modalities as input, we design MVX-LLM, a token-efficient architecture with a Dual Cross-Attention (DCA) projector that fuses the modalities to alleviate information redundancy. Experiments demonstrate that our DCA achieves improved performance under challenging conditions such as foggy (GPTScore: $53.5$ vs. $25.1$ for the baseline).

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BibTeX RIS

Mingzhe Tao, Ruiping Liu, Junwei Zheng, Yufan Chen, Kedi Ying, M. Saquib Sarfraz, Kailun Yang, Jiaming Zhang, Rainer Stiefelhagen. 2026-03-11. DriveXQA: Cross-modal Visual Question Answering for Adverse Driving Scene Understanding. https://arxiv.org/abs/2603.11380

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