Vehicle Road Slipperiness Detection for Collision Risk Mapping
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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
1Measurement precision
If environmental risk factors are made apparent and quantifiable, then identification of high-risk areas improves, but system complexity increases
Solution Approach 1:
The system segments the complex task of risk assessment into distinct components: collision data collection, environmental factor analysis, and risk index calculation. Each component is handled by separate modules that process specific aspects independently, making the overall system more manageable while achieving comprehensive risk identification
Solution Approach 2:
The patent introduces an intermediary risk index calculation layer that bridges raw collision data and environmental factors with actionable safety insights. This intermediary processing layer aggregates multiple data sources and transforms them into a unified risk metric, simplifying the interpretation of complex environmental risk factors
2Reliability
If comprehensive data analysis is performed to identify high-risk areas, then collision reduction improves, but computational resources increase
Solution Approach 1:
The system performs preliminary analysis by pre-processing and storing collision data and environmental factors in organized databases before actual risk assessment is needed. This preliminary organization of data allows for faster, more efficient queries during operational risk assessment, reducing computational burden during critical decision-making moments
Solution Approach 2:
The patent implements a tiered analysis approach where the system performs comprehensive data analysis only for areas flagged as potentially high-risk based on initial screening. This partial analysis strategy focuses computational resources on the most critical areas rather than uniformly analyzing all regions, reducing overall computational requirements while maintaining effectiveness
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.