AI RESEARCH
DriveXQA: Cross-modal Visual Question Answering for Adverse Driving Scene Understanding
arXiv CS.CV
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ArXi:2603.11380v1 Announce Type: new 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