Vehicle Shadow Estimation Using Luminance Gradient Analysis
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Solution Overview
Problem
Existing obstacle identification systems struggle to easily estimate the own-vehicle shadow, particularly when the vehicle is illuminated by sources other than the sun, due to the requirement for precise position and direction information.
Innovation Solution
An obstacle identification apparatus that acquires images from cameras mounted on a vehicle, calculates gradients in luminance values using Sobel filters, and estimates the own-vehicle shadow by identifying the shadow boundary between the vehicle's shadow and external objects based on these gradients.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If position information of the own vehicle, position information of the sun, advancing-direction information of the own vehicle, and three-dimensional-shape information of the own vehicle are used to estimate the own-vehicle shadow, then the estimation accuracy is improved, but the device complexity and difficulty of detection increase
Solution Approach 1:
The patent extracts only the essential luminance gradient information from the image data, ignoring complex 3D shape and position information. By focusing solely on the luminance gradient at the shadow boundary, the system achieves accurate shadow estimation without requiring multiple sensors or complex computational models.
Solution Approach 2:
The patent creates a simplified representation of the shadow by copying only the luminance gradient pattern from the captured image, rather than reconstructing the shadow from 3D vehicle models and sun position data. This copied gradient pattern directly reveals the shadow boundary and extent.
2Measurement precision
If precise position and direction information are required for shadow estimation, then the measurement precision is improved, but the ease of operation deteriorates
Solution Approach 1:
The system uses the luminance information already present in the captured image to automatically identify the shadow boundary, without requiring external input of sun position or vehicle orientation data. The image itself provides all necessary information through its luminance gradients.
Solution Approach 2:
Instead of calculating the shadow position from vehicle and sun data, the patent inverts the approach by directly detecting the shadow boundary from luminance gradients in the image, then using that detected boundary to define the shadow region.
3Adaptability or versatility
If the system must work under various illumination conditions including non-solar sources, then the adaptability is improved, but the difficulty of detecting and measuring increases
Solution Approach 1:
The patent changes the detection parameter from relying on specific light source characteristics (sun position, intensity) to relying on the universal property of shadows: luminance gradients at boundaries. This parameter change makes the detection method adaptable to any illumination source while maintaining simplicity.
Solution Approach 2:
The luminance gradient-based detection method serves as a universal approach that works for shadows cast by any light source (sun, streetlights, headlights), eliminating the need for separate detection mechanisms for different illumination conditions.
Data Source
AI summary
An obstacle identification apparatus acquires an image that is captured by a camera that is mounted to a vehicle. The obstacle identification apparatus calculates a first gradient that is a gradient in a first direction of a luminance value of pixels in the image and a second gradient that is a gradient of the luminance value in a second direction orthogonal to the first direction of the first gradient. Based on the first gradient and the second gradient, the obstacle identification apparatus estimates a shadow boundary that is a boundary between an own-vehicle shadow that is a shadow of the own vehicle and an object outside the vehicle. Based on the estimated shadow boundary, the obstacle identification apparatus estimates the own-vehicle shadow.