Vehicle Structured-Light Recognition for 3D Object Detection at Night
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Solution Overview
Problem
Current methods for recognizing objects in autonomous vehicles, such as LiDAR and camera-based systems, face limitations in accurately detecting three-dimensional shapes and motion, especially at night, and are economically challenging due to the need for multiple sensors and complex deep learning processes.
Innovation Solution
An apparatus using a first and second optical device to generate alternating light patterns, irradiated to an object, and a camera to recognize the patterns, allowing for accurate three-dimensional object recognition and motion state determination, while preventing visual confusion and improving light distribution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If LiDAR is used to recognize objects and measure three-dimensional space, then measurement precision is improved, but device complexity and cost increase due to requiring multiple sensors
Solution Approach 1:
The patent combines the functions of LiDAR (optical device for pattern projection) and camera (image sensor for pattern recognition) into a single integrated system. The optical device projects light patterns while the camera captures reflected patterns, allowing three-dimensional object recognition without requiring multiple separate sensors. This merging achieves LiDAR-level measurement precision while reducing device complexity and cost.
Solution Approach 2:
The camera serves multiple functions: it acts as both an image sensor for capturing visual information and a rangefinder for measuring distance through pattern recognition. By making the camera universal, the system eliminates the need for separate LiDAR sensors, thereby reducing device complexity while maintaining three-dimensional object recognition accuracy.
2Adaptability or versatility
If deep learning is used for object recognition through image sensors, then adaptability is improved, but productivity decreases due to long calculation time
Solution Approach 1:
The system projects predetermined light patterns (such as grid patterns, stripe patterns, or dot patterns) onto objects before capturing images. These pre-designed patterns encode spatial and depth information in a structured format, allowing the control unit to extract object characteristics through simple pattern matching rather than complex deep learning calculations. This preliminary structuring of information maintains adaptability while dramatically improving recognition speed.
Solution Approach 2:
The patent replaces the computational mechanism of deep learning with an optical-mechanical approach using structured light patterns. Instead of using complex algorithms to infer three-dimensional shape from two-dimensional images, the system uses physically encoded patterns that directly represent spatial information. This substitution reduces calculation time while maintaining recognition accuracy.
3Ease of manufacture
If image sensors are used to recognize objects based on chromaticity or luminosity, then ease of manufacture is improved, but measurement precision deteriorates at night when luminosity cannot be distinguished
Solution Approach 1:
The system uses visible light patterns with specific wavelengths (colors) projected by the optical device. By encoding spatial information in the spatial distribution of light patterns rather than relying on object chromaticity or luminosity, the system maintains measurement precision in low-light conditions. The camera detects the reflected light patterns, and the control unit extracts object information from the pattern geometry rather than color or brightness variations.
4Device complexity
If a single optical device projects light patterns, then device complexity is reduced, but object recognition accuracy deteriorates due to visual confusion for drivers and pedestrians
Solution Approach 1:
The patent employs multiple optical devices that alternately project light patterns in a periodic manner. The first optical device projects a first light pattern while the second optical device projects a second light pattern at different time intervals. This periodic alternation prevents visual confusion for drivers and pedestrians (as patterns appear sequentially rather than simultaneously) while maintaining recognition accuracy through temporal separation of pattern projections.
Solution Approach 2:
The system dynamically controls the operation of multiple optical devices, alternating their activation states. The control unit coordinates the periodic projection of different light patterns from different optical devices, creating a dynamic temporal sequence. This dynamic operation prevents visual confusion while enabling the camera to capture distinct patterns for accurate object recognition.
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 solution enables high-accuracy, cost-effective, and rapid object recognition, improving safety and reducing accident rates by combining the strengths of camera and LiDAR techniques, while preventing visual confusion and enhancing driver visibility.
Implementation Method 1
a first optical device configured to generate visible light having a first pattern and irradiate the visible light having the first pattern to an object
Data Source
AI summary
An apparatus for recognizing an object of a vehicle using pattern recognition includes a first lamp including: a first optical device configured to generate light of a first pattern and irradiate the light of the first pattern to an object according to a predetermined cycle, and a second optical device configured to generate light of a second pattern and irradiate the light of the second pattern to the object. The apparatus further includes: a camera configured to recognize the first pattern irradiated to the object when the light of the first pattern is irradiated, and a control unit configured to acquire object recognition information of the object based on the first pattern recognized by the camera.