Vehicle Occupant Classification Using Radar Point Clouds
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Solution Overview
Problem
Current vehicle systems lack effective methods to classify occupants based on radar point cloud data, which limits the ability to dynamically adjust vehicle features according to occupant characteristics, such as size and age, for enhanced safety and comfort.
Innovation Solution
A system utilizing imaging radar sensors to generate radar point clouds, which are analyzed to determine occupant classifications, allowing for the adjustment of vehicle features like audio output limits and passive restraint system settings based on occupant characteristics, using processor-executable instructions to identify, analyze, and implement appropriate operating parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If radar sensors are used to scan vehicle interior regions, then occupant detection capability is improved, but the ability to precisely classify occupants by characteristics remains insufficient
Solution Approach 1:
The patent transforms radar sensor data into three-dimensional point cloud representations, adding spatial dimensionality to the detection process. This dimensional transformation enables precise measurement of occupant characteristics such as height, position, and volume by analyzing the spatial distribution of points in the point cloud, thereby resolving the contradiction between detection capability and classification precision.
2Adaptability or versatility
If vehicle features are configured for different occupant types, then safety and comfort are improved, but the system complexity for identifying and adjusting features increases
Solution Approach 1:
The system automatically classifies occupants based on their radar point cloud characteristics and self-adjusts vehicle features without requiring manual input or complex configuration interfaces. The processor autonomously determines occupant characteristics, selects appropriate feature settings, and implements adjustments, thereby achieving high adaptability while minimizing system complexity.
3Measurement precision
If radar point cloud analysis is implemented, then occupant characteristic determination is improved, but data processing requirements increase
Solution Approach 1:
The system extracts only the essential characteristics needed for occupant classification from the complete radar point cloud data, such as height, position, and volume metrics. By selectively extracting relevant features rather than processing all raw data points, the system achieves precise occupant characteristic determination while reducing computational load and energy consumption.
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
Enables precise classification of vehicle occupants, enabling tailored adjustments to vehicle features, enhancing safety and comfort by accurately determining occupant characteristics from radar point cloud data.
Implementation Method 1
The vehicle can be equipped with radar sensors that can perform radar scans of interior regions of the vehicle
Data Source
AI summary
A system comprises a computer having a processor and a memory, the memory storing instructions executable by the processor to access sensor data from one or more imaging radar sensors of a vehicle, identify, based on the sensor data, a radar point cloud corresponding to an occupant of the vehicle, analyze the radar point cloud to determine an occupant classification of the occupant, determine an operating parameter for a feature of the vehicle based on the occupant classification, and implement the operating parameter for the feature.