Vehicle Headlight Triangulation Baseline Adaptation
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Solution Overview
Problem
The accuracy of distance measurement in vehicle active triangulation systems is limited by the fixed baseline between the headlight and vehicle camera, which restricts the resolution of distance determination due to image processing and calibration errors.
Innovation Solution
The method involves projecting characteristic light patterns from both vehicle headlights onto a projection surface, capturing and processing these patterns to calculate a coverage coefficient, and adjusting their overlap to achieve optimal superposition, thereby increasing the baseline for triangulation and enhancing distance measurement accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the baseline between headlight and vehicle camera is increased to improve distance measurement accuracy, then measurement precision improves, but the fixed vehicle geometry prevents adapting this distance
Solution Approach 1:
The invention divides the triangulation system into two separate triangulation chains: one using the left headlight and vehicle camera, and another using the right headlight and vehicle camera. This segmentation allows each chain to operate independently with its own baseline, effectively utilizing the full width of the vehicle by treating the headlight-camera distance on each side as a separate measurement unit that can be processed independently and combined for improved overall accuracy.
2Measurement precision
If image processing and calibration errors are reduced to improve measurement accuracy, then measurement precision improves, but these errors inherently limit the resolution of distance determination
Solution Approach 1:
The invention implements a feedback mechanism where the system continuously monitors the correlation between characteristic structures of light patterns and headlight units, automatically adjusts alignment parameters, and recalibrates the triangulation calculation. This closed-loop feedback process systematically reduces image processing and calibration errors by detecting deviations and correcting them in real-time, thereby improving measurement precision while compensating for inherent error sources.
Data Source
AI summary
A method for depth perception based on vehicle headlights arranged on both sides of a vehicle includes projecting, by a first vehicle headlight, a first characteristic light pattern onto a projection surface and projecting, by a second vehicle headlight, a second characteristic light pattern. The method also includes capturing an image of the projection surface using an image capturing unit of the vehicle, calculating a frequency representation of the first and second characteristic light patterns in the captured image, and calculating, by evaluating frequency components in the frequency representation of the first and second characteristic light patterns, a coverage coefficient which is correlated with the degree of mutual covering of the first and second characteristic light patterns. The method further includes comparing the coverage coefficient with a coverage threshold value and displacing the first and second characteristic light patterns relative to one another if there is insufficient mutual covering.