Vehicle Camera Depth-of-Field Fusion for Object Distance Detection
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Solution Overview
Problem
Existing optical systems on vehicles struggle to accurately determine the distance of objects in a wide field of view, as two-dimensional sensors can resolve height and length but fail to provide precise distance measurements.
Innovation Solution
Employing a combination of cameras with different depth of fields (DoF) to capture and analyze image data, allowing for the detection of objects in multiple in-focus regions, enabling accurate distance determination through edge detection and machine learning algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a two-dimensional sensor array is used to capture image data, then height and length measures can be resolved, but distance measures of objects cannot be accurately determined
Solution Approach 1:
The patent transitions from two-dimensional image capture to three-dimensional spatial understanding by introducing depth as an additional dimension. This is achieved through multi-camera stereo vision systems that capture images from different spatial positions, enabling the calculation of depth information through triangulation and epipolar geometry relationships.
Solution Approach 2:
The patent introduces computational algorithms as an intermediary between the camera sensors and the final distance measurement. These algorithms process the two-dimensional image data by analyzing disparities, matching features across multiple views, and computing depth maps, thereby converting planar image information into three-dimensional spatial measurements.
2Area of stationary object
If a wide field of view is used to capture more environmental data, then more objects can be detected, but distance determination accuracy decreases
Solution Approach 1:
The patent divides the wide field of view into multiple smaller regions or zones, each processed independently by dedicated computational algorithms. This segmentation allows for more precise local depth calculations in each region while maintaining overall wide coverage, as each segment can be analyzed with higher computational detail.
Solution Approach 2:
The patent adds the depth dimension to the wide field of view data, transforming two-dimensional wide-angle images into three-dimensional spatial information. This is achieved through multi-view geometry and depth map generation, which restores distance information that would otherwise be lost in wide-angle projections.
3Measurement precision
If multiple cameras with different depth of fields are used to determine object distance, then accurate distance measurement is achieved, but device complexity increases
Solution Approach 1:
The patent designs the camera system so that multiple cameras with different depth of fields serve multiple functions: they simultaneously capture images for object detection, distance measurement, and scene context understanding. This multi-functionality reduces the need for separate specialized sensors, thereby managing system complexity while achieving precise distance measurement.
Solution Approach 2:
The patent varies the depth of field parameter across different cameras in the system, with each camera optimized for specific distance ranges. This parameter differentiation allows the system to achieve accurate measurements across a wide range of distances without requiring overly complex individual camera systems, as each camera operates in its optimal parameter range.
Data Source
AI summary
A control circuit that receives first image data from a first camera on a vehicle. The circuit, using the first image data, determines a first in-focus region defined by a first depth of field (DoF) within a first image frame. The control circuit detects an object in the first in-focus region. The control circuit receives second image data from a second camera on the vehicle. Using the second image data, the control circuit detects the object in a second in-focus region, defined by a second DoF, within a second image frame based on the first image data. The second DoF may be greater than the first DoF. The control circuit determines a characteristic of the object from the second image data, and may operate the vehicle using the vehicular operational data.