Multisensor Data Fusion for Static and Dynamic Environment Perception
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Solution Overview
Problem
Current multisensor data fusion methods struggle to accurately perceive both static and dynamic obstacles, leading to inadequate perception performance, especially in advanced driver-assistance systems and automatic driving applications.
Innovation Solution
A multisensor data fusion method that preprocesses feature data by classifying it into static and dynamic components, using reference information from other sensors and historical data to construct accurate static environment and dynamic target information, thereby enhancing perception capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current multisensor data fusion method is used to detect and track moving targets, then dynamic target detection capability is improved, but static obstacle sensing capability deteriorates
Solution Approach 1:
The patent segments the feature data into static feature data and dynamic feature data through data classification. This segmentation allows the system to process static and dynamic targets using different fusion strategies, thereby improving static obstacle detection without compromising dynamic target tracking performance
Solution Approach 2:
The patent implements a dynamic fusion strategy where the fusion process adapts based on the motion state of detected objects. By dynamically adjusting the fusion weights and methods based on whether targets are static or moving, the system optimizes detection accuracy for both categories simultaneously
2Device complexity
If single sensor is used, then device complexity is reduced, but perception performance deteriorates
Solution Approach 1:
The patent merges data from multiple sensor types (camera, millimeter-wave radar, laser radar) into a unified fusion result. By combining the complementary strengths of different sensors - such as the high-resolution imaging of cameras with the all-weather capability of radar - the system achieves superior perception performance that exceeds individual sensor capabilities
Solution Approach 2:
The patent creates a universal data fusion framework that can handle multiple sensor types and multiple target categories (static and dynamic) through a single integrated processing system. This multi-functional approach allows the system to adapt to different sensing requirements while maintaining a cohesive architecture
3Productivity
If data fusion is performed without classification, then processing speed is improved, but measurement precision deteriorates
Solution Approach 1:
The patent performs preliminary classification of feature data into static and dynamic categories before the main fusion process. This preliminary action organizes the data in advance, allowing the subsequent fusion operations to proceed efficiently with pre-categorized information, thereby maintaining both high processing speed and high detection accuracy
Data Source
AI summary
A multisensor data fusion perception method includes receiving feature data from a plurality of types of sensors, obtaining static feature data and dynamic feature data from the feature data, constructing current static environment information based on the static feature data and reference dynamic target information, and constructing current dynamic target information based on the dynamic feature data and reference static environment information such that construction of a dynamic target and construction of a static environment are performed by referring to each other's construction results and the perception capability is for the dynamic target and the static environment that are in an environment in which the moving carrier is located.