A cockpit multi-occupant personalized feature mining method
By collecting multimodal data in the cockpit and performing temporal alignment and hierarchical feature extraction, an inter-occupant correlation matrix and coupled feature tensor are constructed, which solves the shortcomings of feature fusion and modeling in multi-occupant scenarios and achieves high-precision and stable recognition of personalized features.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- RIVOTEK TECH (JIANGSU) CO LTD
- Filing Date
- 2026-04-29
- Publication Date
- 2026-07-21
AI Technical Summary
Existing technologies lack a unified temporal alignment and structured organization method in multi-occupant scenarios, resulting in mismatch of multimodal data information, insufficient modeling of the interaction and relationship between occupants, difficulty in characterizing state synchronization and differences, and lack of a separation mechanism between common features and individual features, which affects the accuracy of personalized modeling.
By deploying visual, physiological signal, and behavioral interaction acquisition units in the cockpit, temporal alignment and hierarchical feature extraction of multi-occupant raw feature datasets are performed, an inter-occupant correlation matrix is constructed, coupled feature tensors are generated, and personalized feature decomposition and cluster analysis are conducted to establish occupant feature evolution sequences and label mapping relationships.
It improves the temporal consistency and information integrity of multi-occupant feature fusion, quantifies the coupling relationship of occupant states, enhances the recognition and expression accuracy of personalized features, and realizes dynamic personalized occupant pattern recognition.
Smart Images

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