Automated Sensor Calibration for Vehicle Steering Accuracy
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Solution Overview
Problem
Current automated steering systems face inaccuracies due to incorrect sensor installation parameters, leading to potential vehicle instability and poor performance, as these parameters are often manually measured and prone to errors like parallax errors.
Innovation Solution
An automated calibration scheme that measures roll, pitch, and yaw rates during vehicle maneuvers to calculate inertial sensor misalignments and offsets, allowing for non-orthogonal electronic control unit installations and reducing operator input by calculating sensor parameters based on raw sensor measurements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If manual measurement of sensor installation parameters is used, then installation process is simple, but measurement precision deteriorates due to parallax errors and operator expertise limitations
Solution Approach 1:
The patent replaces manual mechanical measurement methods with an automated optical/image processing system. The system uses images captured by a camera to automatically detect and calculate sensor installation parameters (distances and angles), eliminating the need for manual measurement tools and operator intervention. This substitution resolves the contradiction by providing both automated precision and operational simplicity.
Solution Approach 2:
The system enables self-calibration by automatically processing images to extract sensor installation parameters without requiring operator expertise. The automated image analysis and parameter calculation perform the measurement task independently, eliminating human error sources while maintaining ease of installation.
2Measurement precision
If automated calibration scheme is implemented, then steering accuracy improves, but device complexity increases due to additional sensors and processing requirements
Solution Approach 1:
The patent makes the existing image capture device serve multiple functions: it is used both for normal operation monitoring and for calibration measurements. By reusing the same camera and processing infrastructure for both purposes, the system achieves automated calibration without adding dedicated calibration hardware, thus improving steering accuracy while limiting complexity increase.
Solution Approach 2:
The system uses copies of existing sensor data and image processing pipelines for calibration purposes. Rather than building entirely new calibration subsystems, it leverages copies of the operational sensor suite and processing algorithms, reducing the complexity overhead of the calibration function.
Data Source
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AI summary
A calibration scheme measures roll, pitch, and yaw and other speeds and accelerations during a series of vehicle maneuvers. Based on the measurements, the calibration scheme calculates inertial sensor misalignments. The calibration scheme also calculates offsets of the inertial sensors and GPS antennas from a vehicle control point. The calibration scheme can also estimate other calibration parameters, such as minimum vehicle radii and nearest orthogonal orientation. Automated sensor calibration reduces the amount of operator input used when calibrating sensor parameters. Automatic sensor calibration also allows the operator to install an electronic control unit (ECU) in any convenient orientation (roll, pitch and yaw), removing the need for the ECU to be installed in a restrictive orthogonal configuration. The calibration scheme may remove dependencies on a heading filter and steering interfaces by calculating sensor parameters based on raw sensor measurements taken during the vehicle maneuvers.