Vehicle Camera Calibration for Accurate Motion Estimation
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Solution Overview
Problem
Current driver assistance systems for attaching a tow vehicle to a trailer require multiple people for alignment, are inefficient, and struggle with accurate motion estimation due to imprecise kinematic parameters like tire diameter and steering ratio, necessitating inconvenient direct measurements or test maneuvers.
Innovation Solution
A method and system for calibrating driver assistance systems by recording and calculating vehicle motion parameters such as wheelbase, track width, tire diameter, and steering offset using camera images and sensor data, allowing for accurate alignment and motion estimation without the need for additional equipment, and enabling periodic recalibration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If direct measurements of vehicle kinematic parameters are performed, then measurement precision is improved, but ease of operation deteriorates due to inconvenience and complexity of the measurement process
Solution Approach 1:
The system uses the vehicle's own existing sensors (cameras, wheel encoders, steering angle sensors) to perform self-calibration of kinematic parameters. The vehicle serves itself by using its operational data to automatically determine accurate parameters without external measurement equipment or complex manual procedures.
Solution Approach 2:
The patent replaces manual mechanical measurement methods with an automated computational system. Instead of physically measuring parameters with tools, the system uses sensor data processing and mathematical calculations to automatically determine kinematic parameters, substituting mechanical measurement with electronic computation.
2Measurement precision
If test maneuvers are executed to determine kinematic parameters, then measurement precision is improved, but loss of time increases due to the need for specific calibration procedures
Solution Approach 1:
The system performs calibration actions preliminarily by using normal driving data collection to prepare the necessary information for parameter determination. Instead of requiring dedicated test maneuvers, the system accumulates data during regular vehicle operation and processes it to establish accurate kinematic parameters in advance.
Solution Approach 2:
The calibration process is designed to be periodic rather than continuous, allowing the system to determine parameters at scheduled intervals using accumulated sensor data. This periodic approach reduces time loss by not requiring constant calibration while still maintaining measurement precision through regular updates.
3Productivity
If automated motion estimation is implemented, then productivity is improved, but measurement precision deteriorates due to inaccurate vehicle kinematic parameters
Solution Approach 1:
The system implements feedback by continuously comparing the motion estimation results with actual sensor measurements and using this information to refine and recalibrate kinematic parameters. The feedback loop ensures that parameter inaccuracies are detected and corrected, maintaining measurement precision while preserving the productivity benefits of automated estimation.
Solution Approach 2:
The system dynamically adjusts kinematic parameters based on accumulated operational data and calibration results. By allowing parameters to change and be updated rather than remaining fixed, the system maintains measurement precision even as vehicle conditions change, thereby sustaining both productivity and accuracy over time.
Data Source
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AI summary
A method of calibrating a driver assistance system comprises entering a calibration mode for a driver assistance system, determining a first estimated position and heading of the vehicle at a first location, capturing an image from a camera and selecting at least one feature in the image. The method of calibrating also includes moving the vehicle to a second location and heading and determining a second estimated position and heading of the vehicle, capturing a second image from a camera and selecting at least one feature in the second image, where the selected feature in the second image the same feature as the selected feature in the first image. The method also includes calculating new vehicle motion parameter values based on the first estimated position and heading, second estimated position and heading, first recorded at least one feature data, and second recorded at least one feature data.