Sensor Data Confidence Estimation via Statistical Analysis
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Solution Overview
Problem
Current autonomous vehicle systems lack effective methods to assess the confidence of sensor data, leading to potential safety risks due to unreliable data from sensors, especially in less obvious failure conditions, which can result in spurious actions or failure to detect obstacles.
Innovation Solution
The system employs a statistical analysis of classifier and feature-set configurations to estimate sensor data confidence by training classifiers on nominal and degraded data sets, calculating confidence thresholds, and using a genetic selection algorithm to generate next-generation configurations, thereby identifying and addressing sensor data integrity issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If redundant sensors are added to perform data verification through cross-correlation, then data integrity is improved, but system cost and complexity increase significantly
Solution Approach 1:
The patent introduces a confidence estimation module as an intermediary component that processes sensor data and generates confidence values. This module uses statistical analysis and machine learning classifiers to assess data reliability without requiring additional redundant sensors, thereby improving data integrity while avoiding the complexity and cost of hardware redundancy
Solution Approach 2:
The patent replaces the mechanical approach of adding redundant physical sensors with an information-processing approach using statistical analysis and machine learning algorithms. The confidence estimation module substitutes physical redundancy with computational methods that analyze existing sensor data to determine its reliability
2Reliability
If periodic monitoring of heartbeat signal and token checking is performed, then obvious sensor failure is detected, but only binary pass/fail signal is provided without confidence values
Solution Approach 1:
The patent performs preliminary analysis of sensor data characteristics during the training phase, establishing baseline statistical models and confidence thresholds before actual operation. This allows the system to proactively assess data quality and generate confidence values rather than merely reacting to obvious failures
Solution Approach 2:
The patent implements a feedback mechanism where the confidence estimation module continuously generates confidence values based on statistical analysis of sensor data. This feedback provides quantitative confidence information that can be used by higher-level decision-making systems to adjust their behavior based on data reliability
3Measurement precision
If statistical analysis with genetic selection algorithm is implemented, then confidence estimation accuracy is improved, but computational processing requirements increase
Solution Approach 1:
The patent performs computationally intensive classifier training and statistical model development during an offline training phase using historical sensor data. This preliminary action allows the system to pre-compute optimal parameters and confidence thresholds, reducing the computational burden during real-time operation to simple statistical comparisons
Solution Approach 2:
The patent transforms the complex problem of confidence estimation into a parameter-based solution by training classifiers to output confidence values based on statistical parameters. Once trained, the system only needs to evaluate these pre-determined parameters rather than performing complex analysis in real-time
Data Source
AI summary
A method and system is provided for estimation of sensor data confidence based on statistical analysis of different classifier and feature-set (CF) configurations. A method may include: training a classifier of a CF configuration based on a training set of nominal sensor data values; executing the classifier on the training set to generate a first set of confidence values; collecting statistics on the confidence values; calculating a confidence decision threshold based on the collected statistics; executing the classifier on an evaluation set of nominal and degraded sensor data values, to generate a second set of confidence values; deciding whether the sensor data values of the evaluation set are nominal or degraded based on a comparison of the second set of confidence values to the confidence decision threshold; and calculating a score to evaluate the trained classifier based on a verification of the decisions.