Vehicle Assistance System Calibration Using AI-Derived 3D Depth Alignment
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Solution Overview
Problem
Existing calibration methods for combining image data and depth data in civilian motor vehicles require a calibration standard with known dimensions and distances, limiting their applicability to specific environments and objects.
Innovation Solution
A method that uses artificial intelligence to derive distances from image data, determine characteristic object sections, and apply a transformation rule to align image and depth data coordinates, enabling calibration without a calibration standard and allowing for arbitrary surroundings and objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a calibration standard with known dimensions and distances is used, then calibration accuracy is improved, but the method is limited to specific environments and objects
Solution Approach 1:
The system uses naturally occurring objects in the environment (buildings, trees, other vehicles) as calibration references instead of requiring external calibration standards. The AI automatically identifies and uses these environmental features for calibration, making the system self-sufficient and adaptable to any environment without predefined calibration objects.
Solution Approach 2:
The calibration method is designed to work with any object or environment that contains recognizable features, rather than requiring a specific calibration standard. This universal approach allows the same calibration process to be applied across diverse scenarios including urban environments, highways, parking lots, and various weather conditions.
2Adaptability or versatility
If artificial intelligence is used to derive distances from image data, then calibration without calibration standard is enabled, but computational complexity increases
Solution Approach 1:
The patent replaces traditional mechanical calibration procedures (physical calibration standards, manual adjustment mechanisms) with an AI-based computational approach. The system uses machine learning models to automatically derive depth information from image data, substituting physical calibration infrastructure with intelligent algorithms that process visual information.
Solution Approach 2:
The AI model acts as an intermediary between the camera images and the depth mapping process. Instead of direct geometric calibration, the AI learns to predict depth relationships from image features, serving as a computational mediator that translates visual information into spatial understanding without requiring explicit calibration data.
3Measurement precision
If image data and depth data are calibrated in real-time, then driver assistance accuracy is improved, but processing time increases
Solution Approach 1:
The system performs preliminary calibration during manufacturing or initial setup, establishing baseline transformation rules between camera and depth sensor coordinate systems. This pre-calibration reduces the computational burden during real-time operation, allowing the system to apply pre-determined calibration parameters rather than performing full calibration calculations continuously.
Solution Approach 2:
The calibration process is designed to be continuous and incremental rather than periodic and complete. The system continuously refines depth mappings using incoming sensor data and AI predictions, maintaining accurate calibration without requiring interruptive recalibration events. This continuous operation ensures real-time performance while maintaining precision.
Data Source
AI summary
A method for calibrating an assistance system of a civilian motor vehicle. The method comprises capturing image data for surroundings of a civilian motor vehicle by means of a camera device in an image sensor coordinate system; capturing depth data indicating first distances between the civilian motor vehicle and objects in the surroundings by means of a depth sensor device; deriving second distances between the civilian motor vehicle and objects represented in the image data by means of image analysis based on artificial intelligence in a data processing device, and creating three-dimensional image data from the image data and the second distances in a three-dimensional image sensor coordinate system; calibrating the image data and the depth data with respect to each other in the data processing device, comprising determining a transformation rule; and applying the transformation rule to subsequently acquired image data. Further, a civilian motor vehicle is provided.