Road Slipperiness Detection Using Vehicle Image Sensors
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Solution Overview
Problem
Existing systems fail to effectively identify and mitigate high-risk areas for vehicle collisions, particularly due to environmental factors that are not readily apparent or quantifiable, leading to unnoticed hazards in transportation infrastructure.
Innovation Solution
A computer-implemented method and system that calculates a risk index for various areas by analyzing historical traffic data and collision records, incorporating factors like expected and observed collisions, road slipperiness, and market penetration, to generate a risk map that highlights high-priority areas for safety improvements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If environmental factors are not monitored or measured, then infrastructure costs are reduced, but safety and reliability deteriorate due to unnoticed hazards
Solution Approach 1:
The system uses existing vehicle-mounted sensors and processors to collect and analyze road condition data. Vehicles themselves serve as mobile monitoring stations, eliminating the need for dedicated infrastructure while improving safety through continuous environmental assessment.
Solution Approach 2:
The patent replaces physical infrastructure-based monitoring systems with a computational approach using image processing and machine learning algorithms. The controller analyzes images from vehicle sensors to detect road conditions, substituting mechanical sensing infrastructure with software-based detection.
2Reliability
If comprehensive safety monitoring systems are implemented, then safety and reliability improve, but device complexity and cost increase
Solution Approach 1:
The system leverages existing vehicle components (sensors, processors, communication modules) that serve multiple functions. The same image sensor used for basic vision tasks also detects road slipperiness, and the existing communication system transmits both navigation data and safety information, reducing overall system complexity.
Solution Approach 2:
The patent combines multiple safety functions into a single integrated system. Image processing, slipperiness detection, risk calculation, and vehicle control are merged into one coordinated system that operates through a single controller, simplifying the architecture compared to separate dedicated systems for each function.
3Loss of information
If environmental risk factors are not quantified, then measurement and detection complexity is reduced, but loss of information increases due to unobservable hazards
Solution Approach 1:
The system transforms physical road conditions (slipperiness, wetness, ice) into quantifiable parameters through image analysis. By converting environmental states into measurable data points like reflectivity, texture patterns, and color values, the system enables information-rich detection without complex measurement apparatus.
Solution Approach 2:
The patent introduces image data as an intermediary between the physical road condition and the detection system. Instead of directly measuring slipperiness with complex sensors, the system captures images that indirectly represent road conditions, then uses image processing to extract meaningful information from these visual intermediaries.
Data Source
AI summary
Systems and methods are disclosed for estimating slipperiness of a road surface. This estimate may be obtained using an image sensor mounted on a vehicle. The estimated road slipperiness may be utilized when calculating a risk index for the road, or for an area including the road. If a predetermined threshold for slipperiness is exceeded, corrective actions may be taken. For instance, warnings may be generated to human drivers that are in control of driving vehicle, and autonomous vehicles may automatically adjust vehicle speed based upon road slipperiness detected.