Vehicle Occupant Classification Using Camera-Radar Position Fusion
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Solution Overview
Problem
Traditional vehicle camera systems fail to assess the position of occupants within the vehicle, leading to potential safety risks due to improper seating, necessitating improved passenger monitoring systems.
Innovation Solution
A classification system utilizing an imaging sensor and radar sensor to capture data, which is processed by a classification algorithm to estimate object key points and bounding boxes, determine 3D locations, and execute response functions such as adaptive restraints, airbag suppression, or alerts based on object classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional camera systems are used to capture occupant position, then image data can be obtained, but the system cannot assess or determine the position of occupants accurately
Solution Approach 1:
The patent combines imaging sensors and radar sensors into a unified monitoring system. The imaging sensor captures visual data while the radar sensor provides spatial position information. By merging these two sensor types, the system achieves both visual recognition and accurate position assessment, resolving the contradiction between capturing images and determining occupant position.
Solution Approach 2:
The patent introduces radar points as an intermediary element that bridges the gap between imaging data and position information. The radar sensor detects electromagnetic waves reflected from occupants to generate radar points, which are then processed to determine 3D positions. This intermediary mechanism enables accurate position assessment without relying solely on traditional camera systems.
2Measurement precision
If multiple sensors and processing algorithms are integrated, then occupant classification accuracy improves, but system complexity increases
Solution Approach 1:
The patent segments the monitoring system into distinct functional modules: imaging sensor module for visual capture, radar sensor module for spatial detection, classification algorithm module for object identification, and response function module for safety actions. Each module performs a specific function and can be independently optimized or replaced, reducing overall system complexity while maintaining high classification accuracy.
Solution Approach 2:
The classification algorithm serves multiple functions: it estimates object key points, determines bounding boxes, identifies 3D locations, and classifies objects based on their positions and characteristics. This multi-functionality reduces the need for separate processing systems, thereby reducing device complexity while improving measurement precision.
3Reliability
If real-time monitoring and classification are performed, then safety response time improves, but computational resources and processing time increase
Solution Approach 1:
The system performs preliminary actions by pre-defining response functions for different object classifications and positions. When objects are detected and classified, the system can immediately execute pre-programmed safety responses without delay. This preliminary preparation ensures rapid safety response while minimizing real-time computational overhead.
Solution Approach 2:
The system continuously monitors object positions and classifications, providing feedback to adjust monitoring parameters and processing priorities in real-time. This feedback mechanism allows the system to focus computational resources on high-risk situations while reducing processing time for low-risk scenarios, thereby maintaining both reliability and efficiency.
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
Effectively classifies vehicle occupants and objects, ensuring safe seating positions and triggering appropriate safety measures, thereby enhancing vehicle safety.
Implementation Method 1
capturing, by an imaging sensor, image data, the image data including an object
Implementation Method 2
capturing, by a radar sensor, radar points corresponding to the object
Data Source
AI summary
A computer-implemented method when executed by data processing hardware causes the data processing hardware to perform operations. The operations include capturing, by an imaging sensor, image data, the image data including an object, capturing, by a radar sensor, radar points corresponding to the object, and projecting, over the captured image data, the captured radar points. The operations also include estimating, by a classification algorithm, at least one of object key points and one or more object bounding boxes, classifying, based on one of the estimated at least one of object key points and one or more object bounding boxes, the object, and executing, in response to the classified object and a position of the object, a response function.