Steering Wheel Interaction Detection Using 3D Point Clouds
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Solution Overview
Problem
Conventional monitoring techniques for vehicle steering wheel interactions rely on visual image data, which may be limited in spatial determination and accuracy.
Innovation Solution
A system utilizing time-of-flight sensors to generate three-dimensional point clouds for detecting and classifying objects on a steering wheel, determining the presence or absence of attachments, and adjusting vehicle operations based on these detections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If visual image data is used for monitoring steering wheel interactions, then the system is simple to implement, but the spatial determination accuracy is limited
Solution Approach 1:
The patent transitions from two-dimensional visual image data to three-dimensional point cloud data by introducing time-of-flight sensors. This dimensional change enables precise spatial determination of objects on the steering wheel, including depth information that visual systems cannot provide, thereby resolving the contradiction between measurement precision and device complexity.
Solution Approach 2:
The patent introduces point cloud data as an intermediary representation between the physical steering wheel environment and the detection system's interpretation. This intermediary format enables accurate spatial mapping and object classification while providing a bridge between the complex sensor inputs and the control system decisions.
2Measurement precision
If time-of-flight sensors are used to generate point clouds, then spatial determination accuracy is enhanced, but the device complexity increases
Solution Approach 1:
The time-of-flight sensor system performs multiple functions: generating point cloud data for spatial mapping, detecting object presence on the steering wheel, classifying objects as hands or articles, and providing input for autonomous control decisions. This multi-functionality justifies the increased device complexity by delivering comprehensive spatial awareness from a single sensor type.
3Measurement precision
If object classification is performed based on point cloud shape analysis, then detection accuracy is improved, but processing time increases
Solution Approach 1:
The system performs preliminary classification by analyzing the shape characteristics of point cloud data to distinguish between hands and articles before triggering autonomous control responses. This preliminary action filters and categorizes data in advance, reducing the computational burden during critical decision-making moments and minimizing processing time loss.
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
Enhances the accuracy and efficiency of detecting and classifying objects on a steering wheel, enabling improved vehicle operation and safety through precise spatial mapping and autonomous control adjustments.
Implementation Method 1
generating, via a time-of-flight sensor, a point cloud representing a compartment of the vehicle. The point cloud includes three-dimensional positional information about the compartment.
Data Source
AI summary
A method for detecting interaction of a user with a steering wheel for a vehicle includes generating, via a time-of-flight sensor, a point cloud representing a compartment of the vehicle. The point cloud includes three-dimensional positional information about the compartment. The method further includes identifying, via processing circuitry in communication with the time-of-flight sensor, the steering wheel in the compartment based on the point cloud. The method further includes comparing, via the processing circuitry, the point cloud to target point cloud data representative of the steering wheel clear of attachments. The method further includes determining one of a presence and an absence of an object on the steering wheel based on the comparison. The method further includes determining an alert condition based on the determination of one of the presence and the absence of the object.