Sensor Calibration Sensitivity Analysis for Robust Prediction Outputs
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Solution Overview
Problem
Raw sensor data from different sensors or the same sensor at different times is not directly comparable, making it challenging to generate accurate prediction outputs, especially in vehicle on-board systems that rely on sensor calibration parameters.
Innovation Solution
A sensitivity analysis system that includes a prediction subsystem and a sensitivity analysis subsystem, which processes raw sensor data and calibration parameters to identify sensitive calibration parameters and updates their values using gradient-based methods, enabling the system to generate more robust prediction outputs even with inaccurate calibration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensor calibration parameters are used to process raw sensor data, then the comparability of sensor data is improved, but the sensitivity to calibration parameter inaccuracies worsens
Solution Approach 1:
The sensitivity analysis subsystem performs preliminary analysis to identify which calibration parameters have the greatest impact on prediction outputs before actual sensing occurs. This allows the system to pre-determine which parameters require high precision and which can tolerate more variation, enabling targeted calibration efforts that improve reliability without requiring all parameters to be perfectly accurate.
Solution Approach 2:
The system dynamically adjusts calibration parameters based on sensitivity analysis results. By identifying sensitive parameters through gradient computation and iterative optimization, the system can focus calibration resources on specific parameters that most affect prediction accuracy, rather than uniformly calibrating all parameters. This selective parameter adjustment resolves the contradiction by maintaining data comparability while reducing overall sensitivity to calibration inaccuracies.
2Measurement precision
If all sensor calibration parameters are calibrated with high precision, then the accuracy of prediction outputs is improved, but the complexity and cost of calibration increases
Solution Approach 1:
Instead of calibrating all sensor calibration parameters with high precision, the sensitivity analysis subsystem identifies only the subset of parameters that have significant impact on prediction outputs. The system performs calibration actions only on these identified sensitive parameters, applying partial calibration effort that is sufficient to achieve acceptable prediction accuracy without the excessive complexity and cost of calibrating all parameters.
Solution Approach 2:
The system transforms the calibration problem from a static process of calibrating all parameters to a dynamic process where parameter importance is continuously assessed through sensitivity analysis. By computing gradients and updating sensitivity scores iteratively, the system adapts which parameters require calibration and to what extent, reducing overall calibration complexity while maintaining prediction accuracy.
3Reliability
If sensitivity analysis is performed to identify critical calibration parameters, then the robustness to calibration errors is improved, but the computational time and processing resources increase
Solution Approach 1:
The sensitivity analysis is performed as a preliminary step before actual sensing and prediction operations. By pre-identifying which calibration parameters are most sensitive, the system avoids performing sensitivity analysis repeatedly during normal operation. This preliminary analysis establishes a calibration strategy that can be applied subsequently without repeated computational overhead, thus improving robustness while minimizing time loss.
Solution Approach 2:
The sensitivity analysis subsystem operates periodically or at scheduled intervals rather than continuously. The system performs sensitivity analysis and calibration parameter identification at specific moments (e.g., during system initialization, after significant changes, or at predetermined time intervals), allowing normal prediction operations to proceed without interruption. This periodic execution maintains robustness while limiting computational time consumption to discrete moments.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining that prediction outputs generated by a prediction system are sensitive to variations in the values of one or more of a set of target sensor calibration parameters. In one aspect, a sensitivity analysis system is configured to perform operations comprising updating the values of one or more target sensor calibration parameters of each sensor data tuple of a plurality sensor data tuples, comprising, for each sensor data tuple: providing the sensor data tuple with the current values of the target sensor calibration parameters to the prediction subsystem to generate a current prediction output; determining a gradient of a function of the current prediction output with respect to the target sensor calibration parameters; and updating the current values of the target sensor calibration parameters of the sensor data tuple using the gradient.