Mono Camera Road Condition Detection via Friction Estimation
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Solution Overview
Problem
Current driver assistance systems lack the ability to reliably and proactively detect road conditions, particularly on wet, snowy, or icy surfaces, due to the absence of friction coefficient measurement, leading to delayed alerts and ineffective interventions.
Innovation Solution
A method utilizing a single mono camera for robust road condition detection, involving image processing algorithms to extract local and global features, combine them, and classify road conditions using a trained classifier, providing estimates of the friction coefficient.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single mono camera is used for road condition detection, then device complexity is reduced, but measurement precision and reliability of friction coefficient estimation deteriorate
Solution Approach 1:
The patent segments the road surface image into multiple regions of interest (ROI), analyzing different areas separately to extract more comprehensive features for friction coefficient estimation. This allows a single camera to capture sufficient information by focusing on specific road surface characteristics in different zones.
Solution Approach 2:
The patent transforms 2D image data from a single camera into 3D road surface information by incorporating depth estimation algorithms and analyzing image gradients, textures, and patterns across multiple scales. This dimensional transformation enables a mono camera to provide measurements previously requiring multi-sensor systems.
2Measurement precision
If multiple sensors (temperature sensor, ultrasound sensor, 3D camera) are used for road condition detection, then measurement precision and reliability improve, but device complexity increases
Solution Approach 1:
The patent makes the single camera system multi-functional by enabling it to perform multiple tasks: capturing road surface images, estimating friction coefficients, detecting road conditions, and providing spatial information. This replaces the need for multiple specialized sensors while maintaining comprehensive monitoring capabilities.
Solution Approach 2:
The system uses the camera's own image data and processing algorithms to generate all necessary road condition information without requiring external sensors. The image processing unit extracts all needed features (textures, patterns, gradients) directly from the camera images, making the system self-sufficient.
3Device complexity
If driver assistance systems operate based on dry road assumptions, then system simplicity is maintained, but safety and effectiveness deteriorate on wet, snowy, or icy roads
Solution Approach 1:
The system proactively detects changes in road conditions before they become critical by continuously analyzing road surface images and estimating friction coefficients in advance. This early detection allows the driver assistance system to adapt its parameters and warnings before accidents occur, rather than reacting too late.
Solution Approach 2:
The system implements continuous feedback by monitoring road condition changes and dynamically adjusting driver assistance system parameters based on the estimated friction coefficient. This closed-loop approach ensures the system adapts to actual road conditions, maintaining safety across varying environments from dry to icy surfaces.
Data Source
AI summary
A method and an apparatus determine a road condition using a vehicle camera. At least one image is acquired using the camera. A first image area that includes an image of the road surface is determined in the at least one image. A classifier assigns the first image area to at least one class (among pre-established classes) that represents a specific road condition of the road surface. Information regarding this specific road condition is output.