Millimeter Wave Radar Visibility Determination via Reflection Intensity
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Solution Overview
Problem
Current millimeter-wave radar models for intelligent vehicle simulation rely on geometry-based methods like culling and ray tracing, which fail to accurately represent the physical characteristics of millimeter wave radar reflections, leading to inadequate object visibility determination in virtual testing.
Innovation Solution
A method that calculates the reflection intensity of objects based on their position, angle, material, shape, and area within the millimeter wave radar's detection area, categorizing vehicles into classes based on their movement direction relative to the host vehicle and determining visibility using a reflection intensity threshold.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If geometry-based methods (culling or ray tracing) are used for object visibility determination, then computation efficiency is improved, but the physical characteristics of millimeter wave radar reflection are not accurately represented
Solution Approach 1:
The patent changes the parameter basis from pure geometric parameters (position, shape) to physical parameters (reflectivity, material properties, surface characteristics). By calculating reflection intensity based on object reflectivity coefficients and surface properties, the model accurately represents millimeter wave radar reflection physics while maintaining computational efficiency through parameterized models rather than complex ray tracing.
2Measurement precision
If reflection intensity calculation based on multiple factors (position, angle, material, shape, area) is implemented, then the fidelity of radar modeling is improved, but the computational complexity increases
Solution Approach 1:
The patent segments the reflection intensity calculation into distinct modular components: geometric visibility determination (first), then reflection intensity calculation (second) based on segmented factors like object reflectivity, surface area, and angular relationships. This segmentation allows each component to be computed independently and efficiently, reducing overall computational complexity while maintaining high fidelity.
Solution Approach 2:
The patent transforms complex physical reflection processes into parameterized calculations using reflectivity coefficients, surface area metrics, and angular parameters. By changing from simulating continuous wave physics to calculating discrete parameter-based reflection intensities, the model achieves high fidelity without excessive computational complexity.
3Speed
If traditional geometry culling methods are used, then simulation speed is maintained, but the visibility determination does not reflect real radar detection characteristics
Solution Approach 1:
The patent performs preliminary geometric culling to quickly eliminate objects that are completely occluded or outside the radar's field of view, maintaining simulation speed. Then, for objects that pass the preliminary geometric check, it calculates reflection intensity using physical parameters. This two-stage approach ensures both speed and reliability by applying detailed physical calculations only where necessary.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enhances the fidelity of millimeter wave radar modeling by accurately simulating the reflection process, improving the accuracy of object visibility determination in intelligent vehicle simulation while meeting simulation efficiency requirements.
Implementation Method 1
object reflection intensity is introduced to determine and analyze object visibility... calculating a reflection intensity of the object... simulates the reflection of waves in real millimeter wave radar detection
Data Source
AI summary
The invention provides a millimeter wave radar modeling-based method for object visibility determination, to solve the problem of object visibility determination based on virtual sensor modeling in intelligent vehicle simulation and testing, and improve the fidelity of sensor modeling while meeting simulation efficiency requirements. It includes the steps of target vehicle information detection to obtain target vehicle information; target vehicle reflection intensity simulation: for each target vehicle performing geometry culling on the target vehicle, discarding completely-invisible target vehicles, and calculating visible section information for each target vehicle that has a visible section, and reflection intensity calculation; target vehicle visibility determination: analyzing visibility of the target vehicle according to the target vehicle reflection intensity, and converting and outputting information of a visible object into an expression form of a real millimeter-wave radar sensing an object.