AR HUD Coordinate Correction for Vehicle Windshield Displays
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Solution Overview
Problem
Augmented reality head-up displays (HUDs) in vehicles suffer from perception errors due to sensor inaccuracies and eye position detection noise, which vary with distance, causing confusion for drivers and affecting the consistency of displayed graphics.
Innovation Solution
The method involves detecting the position of objects and the driver's eye using sensors like radar or lidar, and correcting errors in augmented reality HUD display coordinates using distance-dependent error correction parameters, with low-pass filtering to minimize noise, ensuring accurate and intuitive perception of the driving environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If error correction parameters are applied to HUD display coordinates, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent applies error correction parameters that vary based on object distance to correct HUD display coordinates. Different correction parameters are used for near and far objects, dynamically adjusting the correction magnitude to compensate for perspective distortion and sensor inaccuracies, thereby improving measurement precision without requiring complete system redesign
Solution Approach 2:
The patent replaces complex mechanical calibration systems with computational error correction methods. Instead of physically adjusting HUD components for different viewing conditions, the system uses software-based coordinate correction algorithms that process sensor data and apply mathematical transformations to achieve accurate display positioning
2Stability of the object's composition
If distance-dependent error correction is applied, then perception consistency is improved, but computational complexity increases
Solution Approach 1:
The patent divides the viewing distance range into multiple segments (near, medium, far zones) and applies different error correction parameters for each segment. This segmentation approach maintains perception consistency across varying distances while keeping computational complexity manageable by using discrete correction levels rather than continuous complex calculations
Solution Approach 2:
The error correction parameters are dynamically adjusted based on the detected distance to the observed object. The system automatically transitions between different correction levels as objects move in and out of distance zones, maintaining consistent perception without requiring manual recalibration or complex real-time optimization algorithms
Data Source
AI summary
An augmented reality head-up display (HUD) display method for a vehicle includes: detecting a position of an object outside of the vehicle at which a driver of the vehicle is looking; detecting a position of an eye of the driver while the driver is viewing external object information displayed on a windshield of the vehicle; extracting augmented reality HUD display coordinates of the object based on the detected object position and augmented reality HUD display coordinates of the eye based on the detected eye position; correcting one or more errors in the augmented reality HUD display coordinates of the object and one or more errors in the augmented reality HUD display coordinates of the eye using an error correction parameter for the augmented reality HUD display coordinates of the object and an error correction parameter for the augmented reality HUD display coordinates of the eye, the error correction parameters varying from one another; receiving the corrected augmented reality HUD display coordinates of the object and the corrected augmented reality HUD display coordinates of the eye; and displaying augmented reality HUD graphics of the external object information on the windshield based on the received corrected augmented reality HUD display coordinates.