Highway vehicle-road cooperation data management method and system based on multi-modal perception
By employing multimodal perception scheduling, feature-level dynamic fusion, and blockchain storage technologies, the problems of data collection blind spots and fusion delays in highway vehicle-road cooperative systems have been solved, achieving full-scenario coverage and effective governance of data security.
CN121167650BActive Publication Date: 2026-05-29GUANGZHOU TURINGIT CO LTD
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Patent Information
- Application Number
- CN202511715980.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-21
- Publication Date
- 2026-05-29
- Estimated Expiration
- 2045-11-21
AI Technical Summary
Technical Problem
Traditional data acquisition technologies in highway vehicle-road cooperative systems suffer from blind spots in perception, high data fusion latency, and a lack of traceability mechanisms in data sharing, resulting in insufficient data security and reliability.
Method used
By employing multimodal perception scheduling, feature-level dynamic fusion, digital twin mapping, and blockchain storage technologies, we can achieve full-scene perception, dynamic fusion, and secure and controllable data governance.
Benefits of technology
It improves the effectiveness and security of data fusion, covers all scenarios of data collection, and realizes effective governance of vehicle-road cooperative data.
✦ Generated by Eureka AI based on patent content.
Abstract
The application provides a highway vehicle-road cooperation data management method and system based on multi-modal perception. The method comprises: obtaining a multi-modal vehicle-road cooperation data set and preprocessing to obtain a multi-modal vehicle-road cooperation standard data set, then data extraction to obtain a vehicle-road cooperation effective feature data set, dynamic fusion to generate a vehicle-road cooperation three-dimensional target feature graph, analysis combined with a constructed twin model to obtain road network state change prediction data within a preset time period, generation of lane-level decision data combined with the vehicle-road cooperation three-dimensional target feature graph, and pushing to the corresponding vehicle end, while performing blockchain storage and data access verification on the vehicle-road cooperation effective feature data set; the application covers all scenarios of data collection through adaptive multi-modal perception scheduling, feature-level dynamic fusion, digital twin mapping and blockchain storage technology, improves data fusion effectiveness, and thus realizes effective management of vehicle-road cooperation data.
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