Event Location Estimation in Moving Vehicles Using Motion Sampling
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Solution Overview
Problem
Existing sensor devices in moving vehicles face challenges in accurately determining the occurrence location of events of interest due to the long startup time of motion detection modules, leading to potential misalignment between the detected location and the actual event location, which can impact predictive maintenance and safety.
Innovation Solution
A method and apparatus that estimate the occurrence location of an event of interest by determining a location determination delay period and using motion information acquired at multiple sampling points to calculate the event location, rather than relying solely on the first piece of motion information after startup, while maintaining low power consumption without additional hardware requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the motion detection module is activated immediately upon sensing an event of interest, then the location determination delay is minimized, but the power consumption increases due to frequent activations
Solution Approach 1:
The system performs preliminary actions by pre-calculating the location determination delay period based on historical data and vehicle motion characteristics. This allows the system to predict when the vehicle will be at a specific location without continuously activating the motion detection module, thereby reducing power consumption while maintaining location accuracy.
Solution Approach 2:
The system dynamically adjusts the activation strategy of the motion detection module based on vehicle motion states. When the vehicle is stationary or moving slowly, the module remains inactive to save power. When the vehicle is moving at high speed or undergoes significant motion changes, the module is activated to ensure accurate location determination.
2Loss of time
If the motion detection module startup time is reduced, then the location determination delay decreases, but the measurement precision of event location deteriorates due to insufficient sampling
Solution Approach 1:
The system performs preliminary sampling of motion information before the actual event occurs by monitoring vehicle motion characteristics in advance. This preliminary data is used to predict the vehicle's position at the time of the event, allowing the system to determine location accuracy without waiting for the motion detection module to fully start up.
Solution Approach 2:
The system introduces an intermediary calculation method that uses the pre-acquired motion information and vehicle dynamics models to estimate the event location. This intermediary approach bridges the gap between the event occurrence time and the motion detection module startup time, maintaining measurement precision without requiring immediate module activation.
3Measurement precision
If multiple sampling points are used to calculate event location, then the location accuracy improves, but the computational complexity and processing time increase
Solution Approach 1:
The system extracts only the essential motion parameters from the multiple sampling points that are most relevant to location determination. By selecting key sampling moments based on vehicle motion characteristics rather than processing all available data points, the system maintains high location accuracy while significantly reducing computational complexity.
Solution Approach 2:
The system changes the parameter representation by transforming multiple motion sampling points into a simplified set of characteristic parameters that capture the essential vehicle trajectory information. This parameter transformation allows accurate location calculation with reduced computational burden.
Data Source
AI summary
A method and apparatus for estimating an occurrence location of an event of interest, and computing devices. The method may include: determining a location determination delay time period based on a first time point at which the event of interest occurs and a second time point at which motion information is acquired earliest; acquiring pieces of motion information at sampling time points with a predetermined sampling interval, each piece of motion information includes a motion speed, a motion direction, and a location of the moving vehicle at a respective sampling time point; determining a motion change rate of the moving vehicle based on the pieces of motion information; and estimating the occurrence location of the event of interest based on the motion information at an ordinal first one sampling time point of the sampling time points, the motion change rate, and the location determination delay time period.