Motion Estimation Model With Confidence-Based Relearning
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Solution Overview
Problem
Existing motion estimation techniques for work vehicles in unknown environments require frequent model relearning, leading to inefficient operation as the vehicle must stop to update models, and current systems struggle to accurately estimate motion in such environments.
Innovation Solution
A motion estimation apparatus that generates motion analysis data, analyzes the environment, estimates motion using a model, and sets a confidence interval to determine when relearning is necessary, allowing the vehicle to continue operating without frequent model updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the work vehicle transitions to a mode for relearning a model when new motion analysis data is obtained, then the accuracy of motion estimation is improved, but the work vehicle cannot be efficiently operated due to frequent stops
Solution Approach 1:
The system changes the parameter of model confidence by comparing new motion analysis data against a threshold value. When the data falls within the confidence interval of the current model, no relearning is triggered. This parameter-based decision mechanism allows the system to maintain high measurement precision while avoiding unnecessary stops, thus resolving the contradiction between accuracy and operational efficiency
Solution Approach 2:
The system implements a feedback mechanism where motion analysis data is continuously monitored and compared against the current model's confidence interval. This feedback loop enables the system to determine whether relearning is necessary based on actual performance data, allowing the vehicle to maintain efficient operation while ensuring motion estimation accuracy when deviations occur
2Reliability
If the model is relearned frequently to ensure accurate motion estimation in unknown environments, then the reliability of motion estimation is improved, but the operation efficiency deteriorates
Solution Approach 1:
The system uses a confidence interval parameter to determine when relearning should occur. By setting a threshold for acceptable deviation, the system maintains reliable motion estimation only when necessary, avoiding unnecessary time loss while ensuring accuracy when environmental conditions change significantly
Solution Approach 2:
Instead of continuously relearning the model, the system performs partial updates only when motion analysis data falls outside the confidence interval. This selective approach ensures reliability is maintained when needed while minimizing the time lost to relearning operations
Data Source
AI summary
A motion estimation apparatus includes: a motion analyzing unit that generates first motion analysis data representing actual motion of a mobile object in a first environment; an environment analyzing unit that analyzes the first environment based on environment state data representing a state of the first environment, and generates environment analysis data; an estimation unit that inputs the environment analysis data to a model for estimating motion of a mobile object in the first environment, and estimates the motion of the mobile object in the first environment; and a learning instruction unit that sets a confidence interval, based on the motion estimation result data estimated by the model, and if the first motion analysis data is not in the set confidence interval, instructing a learning unit that learns the model to relearn the model.