Multimodal action understanding requires complementary sensory observations to be connected with structured semantic knowledge. This is particularly challenging in vehicle cabins, where fine-grained actions exhibit similar upper-body motion, relevant objects may be small or occluded, and appearance varies substantially across illumination conditions. Existing in-cabin datasets, however, rarely provide synchronized visual modalities together with dense object annotations and explicit action--object alignment, limiting the study of object-grounded multimodal recognition. To address this gap, we introduce the Driver Action with Object Synergy (DAOS) dataset, containing 9,787 video clips, 36 fine-grained actions, 15 object categories, and more than 2.5 million annotated object instances. DAOS provides synchronized RGB, infrared, and depth observations from four camera views, thereby combining complementary appearance, low-light, and geometric information. Building on DAOS, we propose the Action-Object-Relation Network (AOR-Net), a vision-language framework that organizes multimodal understanding at three semantic levels: action, object, and human-object relation. Its Chain-of-action Prompting mechanism progressively grounds global motion with localized object and relation cues, while a textual prototype bank supplies corresponding semantic descriptions. A Mixture of Thoughts module then dynamically selects and integrates the most informative semantic levels and visual modalities under object-rich and object-scarce conditions. Extensive experiments show that AOR-Net consistently outperforms strong baselines on DAOS, demonstrating the effectiveness of combining heterogeneous visual sensing with structured action-object-relation semantics for fine-grained multimodal video understanding.

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