Camera Simulation Validation for Object Recognition
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Solution Overview
Problem
Existing camera simulation devices for evaluating the operability of object recognition systems in vehicles are costly and require frequent reconfiguration for different types of object recognition devices, limiting their versatility and realism in simulating various driving scenarios.
Innovation Solution
A method using a camera simulation device to generate simulation image data, which is compared with reference image data based on predetermined comparison measures such as color intensity, brightness, contrast, and edge contour direction, allowing for validation of the simulation device's suitability for evaluating object recognition systems independently of the specific object recognition device, enabling reliable and cost-effective evaluation across different types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If film recordings from test drives are used to evaluate object recognition devices, then the evaluation data is realistic and accurate, but the cost and time required for extensive test drives increases significantly
Solution Approach 1:
The patent creates virtual copies of real-world driving scenarios through simulation. Instead of physically capturing images during actual test drives, the system generates synthetic image sequences that replicate real driving conditions, objects, and environments. This copying approach maintains evaluation realism while eliminating the need for extensive physical test drives.
Solution Approach 2:
The patent replaces the mechanical system of physical test drives with a computational simulation system. Rather than using cameras mounted on vehicles to capture real-world images during actual driving, the system uses computer-generated imagery to create virtual driving scenarios, substituting physical mechanics with digital simulation.
2Loss of time
If a camera simulation device is used to generate test data, then costs and time are reduced, but the simulation data may lack realism and fail to accurately represent real camera characteristics
Solution Approach 1:
The patent performs preliminary characterization of real camera systems by capturing reference images with actual cameras under controlled conditions. These reference measurements of camera characteristics (distortions, noise, resolution) are obtained in advance and stored for use in the simulation, ensuring the virtual camera accurately replicates real camera behavior without requiring repeated physical testing.
Solution Approach 2:
The patent implements a feedback mechanism where the simulation device generates virtual camera images that are compared against reference images captured by real cameras. The differences between simulated and real camera characteristics are used to iteratively adjust and refine the simulation parameters, ensuring the virtual camera progressively matches the behavior of actual camera systems.
3Measurement precision
If the simulation device is adapted to a specific object recognition device type, then the evaluation is optimized for that device, but the simulation device requires frequent reconfiguration for different device types
Solution Approach 1:
The patent designs the simulation device with universal applicability across multiple object recognition device types. Rather than creating specialized simulations for each device, the system implements a generalized virtual camera framework that can evaluate different recognition algorithms and device configurations using the same simulation infrastructure, eliminating the need for frequent reconfiguration.
Solution Approach 2:
The patent separates the simulation functionality into independent modular components: the virtual camera generation module, the object recognition device interface, and the evaluation module. This segmentation allows different object recognition devices to be connected to the same simulation system without requiring changes to the core simulation engine, enabling versatile evaluation across device types.
Data Source
AI summary
For testing an object recognition device for a motor vehicle at reasonable costs for different routes, image data for testing the object recognition device may be generated with a camera simulation device. Because the image data of a camera simulation device are artificially generated, it must be made certain that they have a realistic effect on the object recognition device. Reference image data are generated with a camera and simulation image data are generated with the camera simulation device for at least one route. The simulation image data and the reference image data are compared with each other based on at least two comparison measures. A value which is independent of the object recognition device to be tested can be determined for each of the comparison measures. It is then checked if the totality of the generated comparison values satisfies a predetermined validation criterion.