Windshield Optical Distortion Simulation for ADAS Camera Calibration
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Solution Overview
Problem
Existing methods for calibrating advanced driver-assistance systems (ADAS) fail to accurately account for the intrinsic optical properties of individual windshields, leading to inefficient calibration and potential safety failures due to optical distortions, and require cumbersome and expensive on-site systems.
Innovation Solution
A method combining stochastic ray tracing and machine learning to simulate the effects of windshield distortions on image quality, using measured optical quality functions and convolutional neural networks to accurately predict image distortions without high computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If stochastic ray tracing is used to simulate windshield distortions, then measurement precision and reliability are improved, but computational resources and processing time increase
Solution Approach 1:
The patent pre-calculates and stores distortion maps for various windshield positions and orientations during a calibration phase. These pre-computed distortion characteristics are then reused during real-time operation, avoiding repeated complex ray tracing calculations while maintaining accurate distortion simulation.
Solution Approach 2:
The patent creates simplified copies of the complex optical distortion effects by generating lookup tables and distortion maps that replicate the behavior of full stochastic ray tracing. These copies enable fast simulation without requiring the full computational power of original ray tracing algorithms.
2Measurement precision
If individual windshield optical properties are measured and simulated, then calibration accuracy is improved, but device complexity and measurement time increase
Solution Approach 1:
The patent develops a multi-functional calibration system that can measure different windshield properties (refractive index, thickness, curvature) using a unified measurement apparatus. The system adapts its measurement protocol based on the specific windshield type, reducing the need for multiple specialized devices while maintaining individualized calibration accuracy.
Solution Approach 2:
The patent measures and stores multiple optical parameters of each windshield (refractive index, thickness, curvature radius, surface quality) and uses these parameters to dynamically adjust the distortion simulation. By changing and utilizing multiple parameters, the system achieves high calibration accuracy without requiring overly complex measurement equipment for each individual parameter.
3Productivity
If real-time distortion simulation is implemented, then ADAS calibration efficiency is improved, but processing speed and computational load increase
Solution Approach 1:
The patent performs computationally intensive distortion simulations during an offline calibration phase to generate pre-computed distortion maps and correction parameters. During real-time ADAS operation, these pre-computed data structures are simply looked up and applied, achieving fast processing while maintaining the benefits of accurate individual windshield simulation.
Solution Approach 2:
The patent implements a dynamic calibration approach where the level of simulation detail adapts based on operational requirements. For routine operations, simplified distortion models are used for speed, while more detailed simulations are performed only when necessary, balancing processing speed and calibration efficiency dynamically.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The method effectively simulates windshield distortions in real-time, improving calibration accuracy and reducing resource requirements, enabling efficient production and on-board system implementation.
Implementation Method 1
The method combines stochastic ray tracing and machine learning to simulate the effects of windshield distortions on image quality
Implementation Method 2
measuring the optical quality of the windshield by means of stochastic ray tracing, wherein a number of virtual light rays are propagated through the windshield
Data Source
AI summary
A method for simulating the effects of the optical quality of windshield onto the image recording quality of a digital image recording device, in particular a digital image recording device for an advanced or automated driver-assistance system. The method is based on a combination of an adapted stochastic ray tracing method and of an adapted machine learning method to simulate the effects of the optical distortions of a windshield on the image recording quality of a digital image recording device. The simulation is performed from a measured optical quality function of the windshields related to those optical distortions.