Multi-Sensor Fusion Matrix for Environment Perception
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Solution Overview
Problem
Current sensor fusion technologies in environmental sensing and target detection lack deep fusion of data from multiple sensors, leading to insufficient data dimensionality and integrity, which hinders effective environment perception and target detection.
Innovation Solution
A multi-sensor fusion method that combines image data from image acquisition sensors with probe data from other sensors like radar and Lidar into a multi-dimensional matrix structure, expanding each pixel's information dimensions to include distance, velocity, and thermal radiation data, enabling deeper data fusion and feature extraction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If video recording method is used to capture target information, then the completeness of event recording is improved, but the data transmission bandwidth and storage space requirements increase significantly
Solution Approach 1:
The patent extracts only the essential event information (target position, speed, acceleration, key events) from continuous video data, rather than storing complete video streams. This extraction approach maintains event recording completeness while dramatically reducing data volume for transmission and storage.
Solution Approach 2:
The system performs preliminary processing of sensor data to identify and mark key events before storage or transmission. By pre-processing data to extract only relevant event information, the system reduces subsequent data handling requirements while preserving all necessary event details.
2Loss of information
If data from multiple sensors is collected independently, then the data dimensionality is maintained, but the deep fusion capability and environment perception ability are insufficient
Solution Approach 1:
The patent merges data from multiple sensors (camera, radar, ultrasonic, Lidar) into a unified data structure with consistent coordinate systems and time synchronization. This combining approach preserves all original data dimensions while enabling deep fusion through unified processing, thereby improving environment perception reliability.
Solution Approach 2:
The patent adds temporal and spatial dimensionality to sensor data by incorporating time stamps, position coordinates, and motion vectors. This dimensional enhancement transforms independent sensor readings into rich multi-dimensional event data that enables sophisticated environment perception and target tracking.
3Ease of manufacture
If traditional sensor data structures are used, then the simplicity of data collection is maintained, but the feature extraction and data mining capabilities are limited
Solution Approach 1:
The system performs preliminary computation of derived parameters (speed, acceleration, relative position) directly at the data collection stage. This preliminary action enriches the raw sensor data with valuable features that would otherwise require complex post-processing, thereby enhancing data mining capability while maintaining collection simplicity.
Data Source
AI summary
A data processing method, device and multi-sensor fusion method for multi-sensor fusion, which can group data captured by different sensors in different probe dimensions to simultaneous interpreting deep learning data based on pixel elements in the multi-dimensional matrix structure, thereby realize the more effective data mining and feature extraction to support more effective ability of environment perception and target detection.