Multi-Scale Road Surface Detection Fusion
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Solution Overview
Problem
Existing road surface detection systems face challenges in accurately identifying snow-covered surfaces due to varying image scales, which can lead to incomplete detection of precipitation conditions, resulting in reduced traction and stability issues for vehicles.
Innovation Solution
A method that fuses sensor data from multiple scales, utilizing feature-based, decision-based, and hybrid fusion techniques to analyze and classify road surface conditions, providing higher resolution for localized areas and general conditions, thereby enhancing detection accuracy and confidence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single scale of sensor data is used for road surface detection, then the detection process is simple, but the detection accuracy is reduced due to inability to capture both localized and general road conditions
Solution Approach 1:
The road surface detection is segmented into multiple scales: local scale for high-resolution localized conditions and global scale for general road conditions. This segmentation allows the system to capture both detailed patch information and overall road state, resolving the contradiction between detection accuracy and processing complexity by organizing the analysis into hierarchical levels.
Solution Approach 2:
The patent merges local scale sensor data and global scale sensor data through fusion techniques. By combining the high-resolution localized information with the broader contextual information, the system achieves comprehensive detection accuracy that neither scale could provide alone, while the fusion process manages the complexity through structured integration.
2Measurement precision
If local scaled sensor data is used alone, then high resolution is achieved for confined sections, but the general road conditions are missed
Solution Approach 1:
The patent adds a spatial dimension to the analysis by incorporating both local and global scales. This dimensional expansion allows the system to simultaneously examine confined sections with high resolution while also capturing the broader road context, preventing information loss about general conditions while maintaining detailed localized analysis.
3Measurement precision
If global scaled sensor data is used alone, then general road conditions are captured, but localized snow patches are missed
Solution Approach 1:
The detection system segments the road surface analysis into hierarchical scales, with global scale providing broad coverage and local scale providing detailed examination of specific areas. This segmentation ensures that localized snow patches are not missed by the global view, while the global context is preserved through the hierarchical structure.
4Reliability
If traditional single-scale detection is used, then the system response is fast, but detection confidence is reduced due to incomplete information
Solution Approach 1:
The system performs preliminary processing of sensor data at multiple scales simultaneously, extracting features and preparing information in advance. This preliminary action at the data processing stage enables comprehensive analysis without significantly delaying the final detection response, maintaining reliability while managing processing time through parallel operations.
Data Source
AI summary
A method of determining a surface condition of a path of travel. A plurality of images is captured of a surface of the path of travel by an image capture device. The image capture device captures images at varying scales. A feature extraction technique is applied by a feature extraction module to each of the scaled images. A fusion technique is applied, by the processor, to the extracted features for identifying the surface condition of the path of travel. A road surface condition signal provide to a control device. The control device applies the road surface condition signal to mitigate the wet road surface condition.