3D Camera Road Friction Estimation via Height Profile Analysis
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Solution Overview
Problem
Existing methods for determining the coefficient of friction, such as those using lidar sensors, are costly, require precise aiming, and fail to provide adequate advance warning, especially under wet conditions.
Innovation Solution
A 3D camera captures images of the surroundings to create a road surface height profile, allowing for the estimation of the coefficient of friction by identifying features like water reflections, tire tracks, and surface noise patterns, with optional support from 2D image data and edge detection methods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If lidar sensors are used to determine the coefficient of friction, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent uses a camera to capture optical images of the road surface, creating a visual copy that can be analyzed to infer friction characteristics. Instead of directly measuring friction with complex lidar sensors, the system captures images and uses image processing to determine surface properties, thereby achieving friction estimation with simpler, more cost-effective equipment while maintaining measurement capability
Solution Approach 2:
The patent replaces the mechanical/optical measurement system of lidar with an imaging-based system using cameras. The camera captures road surface images, and through image processing algorithms, the system extracts friction-relevant features such as texture, reflections, and surface patterns, substituting direct physical measurement with optical imaging and computational analysis
2Measurement precision
If lidar sensors are used to determine the coefficient of friction, then measurement precision is improved, but the ability to estimate friction in advance is reduced
Solution Approach 1:
The patent enables advance estimation of the coefficient of friction by capturing road surface images before the vehicle reaches the measured location. The image processing system analyzes road surface characteristics in real-time, allowing the control system to prepare appropriate responses in advance, thereby gaining time advantage over systems that require direct measurement at the exact moment
3Measurement precision
If additional lidar sensors are installed, then measurement precision is improved, but cost increases
Solution Approach 1:
The patent makes the camera system multi-functional by using it for both road surface imaging and friction estimation. The same imaging hardware that captures visual information about the road can be processed to extract friction characteristics, eliminating the need for dedicated expensive sensors and reducing overall system cost while maintaining measurement capability
Solution Approach 2:
The patent replaces expensive, complex lidar sensors with more affordable camera systems. While cameras may have lower individual component costs, the system achieves comparable functional outcomes through sophisticated image processing algorithms, making the overall solution more cost-effective for mass production in vehicles
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
This approach enables a more reliable, anticipatory, and cost-effective estimation of the road surface friction, improving safety in driver assistance systems like emergency braking.
Implementation Method 1
areas of water are recognized as reflections on the road from the height profile of the road surface. This is because reflections due to a film of water or a puddle can be assigned to areas that are lower in the height profile than the real road surface
Data Source
AI summary
The invention relates to a method and device for estimating coefficients of friction using a 3-D camera. At least one image of the environment of the vehicle is recorded by means of the 3-D camera. A height profile of the road surface is created in the entire area in front of the vehicle from the image data of the 3-D camera. The anticipated local coefficient of friction of the road surface in the area in front of the vehicle is estimated from the height profile.