Vehicle Velocity Estimation via Dynamic Confidence Filtering
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Solution Overview
Problem
Existing vehicle state estimation systems, relying on inertial monitoring units, face challenges with noise susceptibility and require heavy filtering, leading to poor estimations in transient conditions, especially when measuring vehicle acceleration in six degrees of freedom.
Innovation Solution
A controller-based system that monitors vehicle operating parameters, calculates confidence values in vehicle reference velocity by determining the rate of change of these parameters, and applies filters to generate a dynamic filter coefficient for improved estimation accuracy, using parameters like longitudinal acceleration, throttle position, and wheel slip measurements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If heavy filtering is applied to reduce noise in vehicle sensor signals, then measurement precision is improved, but reliability deteriorates in transient conditions
Solution Approach 1:
The filter coefficient is dynamically adjusted based on the confidence value, which is calculated from the rate of change of vehicle operating parameters. When the rate of change exceeds a threshold (indicating transient conditions), the filter coefficient is reduced to allow faster response. This dynamic adaptation resolves the contradiction by making the filtering strength variable rather than fixed, preserving reliability during transients while maintaining precision during steady-state operation.
2Device complexity
If the number of degrees of freedom in IMU measurement is reduced, then device complexity is reduced, but measurement precision deteriorates
Solution Approach 1:
The system calculates a confidence value based on the rate of change of vehicle operating parameters and uses this feedback to dynamically adjust the filter coefficient. This feedback mechanism compensates for the reduced measurement capabilities by adaptively tuning the filtering to maintain estimation accuracy despite the simplified IMU configuration.
3Measurement precision
If signal filtering is increased to reduce noise, then measurement precision is improved, but productivity deteriorates due to poor transient estimation
Solution Approach 1:
The system changes the filter coefficient parameter dynamically based on the confidence value derived from the rate of change of operating parameters. During transient conditions, when the rate of change is high, the filter coefficient is reduced to improve transient estimation performance. During steady-state operation, the filter coefficient is increased to reduce noise. This parameter adaptation resolves the contradiction between noise reduction and transient performance.
Data Source
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AI summary
The present disclosure relates to apparatus (101) for estimating confidence in a vehicle reference velocity (V). The apparatus includes a controller having an electronic processor (121) having an electrical input for receiving at least one first vehicle operating parameter; and an electronic memory device (123) electrically coupled to the electronic processor and having instructions stored therein. The electronic processor (121) is configured to access the memory device (123) and execute the instructions stored therein such that it is operable to monitor the at least one first vehicle operating parameter; and to calculate a confidence value (F1) representing the confidence in the vehicle reference velocity, the confidence value (F1) being calculated in dependence on the vehicle operating parameter.