Vehicle Sensor Calibration Using Hough-Based Driving Axis Alignment
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Solution Overview
Problem
Calibrating vehicle sensors in motor vehicles is time-consuming and costly, especially when aligning sensors with the driving axis in production environments, and existing methods struggle with dynamic calibration and accuracy, particularly in elevation direction and with multisensor systems.
Innovation Solution
A method and device that utilize sensor data from moving vehicles to compute object positions, apply Hough transformation, and calibrate sensors relative to the driving axis without requiring radial velocities or fixed objects, enabling dynamic calibration without downtime and improving accuracy, especially in elevation direction, by using a computing device and calibration unit to process sensor data and correct static angle deviations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If static calibration methods are used to align sensors with the driving axis, then measurement precision is improved, but productivity deteriorates due to time-consuming procedures and vehicle downtime
Solution Approach 1:
The patent transitions from static calibration to dynamic calibration by performing sensor alignment measurements while the vehicle is in motion. The system captures sensor data at multiple measurement times during vehicle movement, computes object positions dynamically, and determines alignment through Hough transformation of these dynamic measurements, thereby eliminating vehicle downtime while maintaining precision.
Solution Approach 2:
The system performs preliminary actions by capturing sensor data at multiple measurement times during normal vehicle operation before final calibration is needed. This preliminary data collection during dynamic operation allows the calibration to be completed without requiring separate static measurement sessions, improving productivity while maintaining accuracy.
2Productivity
If dynamic calibration with stationary objects is used, then productivity is improved by avoiding downtime, but measurement precision deteriorates due to difficulty in monitoring object positions
Solution Approach 1:
The system implements feedback by continuously monitoring the positions of stationary objects relative to each other using the vehicle sensor during dynamic operation. This feedback mechanism ensures that object positions are tracked and verified, maintaining measurement precision while enabling productivity improvement through dynamic calibration without downtime.
Solution Approach 2:
The patent replaces mechanical monitoring systems with computational methods by using the vehicle sensor to optically or electromagnetically detect and compute object positions. The Hough transformation computationally analyzes the sensor data to determine alignment, substituting complex mechanical position monitoring with mathematical processing, thereby maintaining precision while improving productivity.
3Productivity
If trilateration via stationary objects is used for dynamic calibration, then productivity is improved, but device complexity increases due to difficulty in monitoring object positions
Solution Approach 1:
The system applies universality by using the vehicle sensor for multiple functions: both for detecting stationary objects during dynamic calibration and for normal vehicle operation. This multi-functionality reduces the need for separate dedicated calibration equipment, thereby reducing device complexity while maintaining productivity improvements from dynamic calibration.
Solution Approach 2:
The patent uses copying by creating a computational model (Hough transformation) that represents the spatial relationships of detected objects. This mathematical copy of the physical object positions allows for simplified analysis and alignment determination without requiring complex physical monitoring systems, reducing device complexity while enabling dynamic calibration.
4Measurement precision
If static calibration methods are used for multisensor systems, then measurement precision is maintained, but device complexity increases linearly with each sensor
Solution Approach 1:
The system merges the calibration process for multiple sensors into a unified dynamic procedure. Instead of calibrating each sensor separately through static methods, the system performs dynamic calibration for all sensors simultaneously by capturing data from multiple measurement times and processing them together through Hough transformation, thereby reducing device complexity while maintaining precision for multisensor systems.
Data Source
AI summary
A method for calibrating a vehicle sensor of a motor vehicle. The method includes the steps: ascertaining, by way of the vehicle sensor, sensor data at a plurality of measurement times, the motor vehicle moving in relation to objects in surroundings of the motor vehicle; computing object positions of the objects on the basis of the ascertained sensor data; computing a Hough transformation on the basis of the computed object positions; ascertaining an alignment of the vehicle sensor in relation to a driving axis of the motor vehicle on the basis of the computed Hough transformation; and calibrating the vehicle sensor on the basis of the ascertained alignment of the vehicle sensor in relation to the driving axis of the motor vehicle.