Vehicle Occupant Sensing With Selective 3D Sensor Activation
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Solution Overview
Problem
Vehicles equipped with three-dimensional image sensors consume more computational resources, limiting the allocation to other sensors and components, while two-dimensional image sensors provide less precise position data, necessitating the use of machine learning programs for depth data estimation with potential inaccuracies.
Innovation Solution
A system that combines two-dimensional and three-dimensional image sensors, where the computer selectively activates the three-dimensional image sensor to collect reference data for retraining the machine learning program, allowing it to generate precise three-dimensional position data with the computational efficiency of two-dimensional image sensors, thereby optimizing resource allocation and reducing power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a three-dimensional image sensor is used continuously, then position data precision is improved, but computational resource consumption increases
Solution Approach 1:
The system implements periodic activation of the three-dimensional image sensor based on detected events or time intervals. The sensor is activated only when needed (e.g., when an event is detected by other sensors or at scheduled intervals) rather than continuously, thereby reducing computational resource consumption while maintaining position data precision when required.
Solution Approach 2:
The system uses its own operational data (detected events, current state) to automatically control sensor activation. When specific conditions are met (e.g., detected events indicating occupant movement or position changes), the system self-triggers the three-dimensional sensor to capture updated position data, eliminating the need for continuous operation.
2Use of energy by moving object
If a two-dimensional image sensor with machine learning program is used, then computational resource consumption is reduced, but position data precision deteriorates
Solution Approach 1:
The system merges the capabilities of two-dimensional image sensors (for continuous, low-computational monitoring) with periodic three-dimensional image sensor activation (for high-precision position data). This combination allows the system to maintain reduced computational resource consumption while obtaining precise position data when needed through the integrated sensor system.
Solution Approach 2:
The system uses detected events as an intermediary trigger mechanism. When events are detected by sensors or processed by the machine learning program, these events serve as intermediaries that trigger the activation of the three-dimensional sensor, thereby bridging the gap between continuous monitoring and periodic high-precision measurement.
3Productivity
If the three-dimensional image sensor is activated selectively based on detected events, then resource allocation is optimized, but sensor response time may increase
Solution Approach 1:
The system performs preliminary actions by continuously monitoring for detected events using lower-power sensors and processing mechanisms. When specific event conditions are met in advance, the system triggers the three-dimensional sensor activation, ensuring that high-precision position data is captured at the optimal moment while maintaining efficient resource allocation throughout the monitoring period.
Data Source
AI summary
A two-dimensional image of a vehicle occupant in a vehicle is collected. The collected two-dimensional image is input to a machine learning program trained to output one or more reference points of the vehicle occupant, each reference point being a landmark of the vehicle occupant. One or more reference points of the vehicle occupant in the two-dimensional image is output from the machine learning program. A location of the vehicle occupant in an interior of the vehicle is determined based on the one or more reference points. A vehicle component is actuated based on the determined location. For each of the one or more reference points, a similarity measure is determined between the reference point and a three-dimensional reference point, the similarity measure based on a distance between the reference point and the three-dimensional reference point.