Intersection Collision Prediction Using Telematics and Map Data
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Solution Overview
Problem
Conventional techniques for assessing roadway safety often focus on the risk associated with intersecting roadways rather than the roadway intersection itself, leading to inaccurate risk assessments and potentially unsafe routing decisions.
Innovation Solution
A system that uses telematics data and map data to identify roadway intersections, determine intersection metrics, hazard ratings, and generate collision probabilities using a machine learning model, providing a more accurate assessment of collision risk at individual intersections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional techniques focus on risk associated with intersecting roadways rather than the intersection itself, then the assessment process is simpler, but the accuracy of risk assessment deteriorates
Solution Approach 1:
The patent segments the roadway network into individual intersection units, each with its own unique risk profile. Instead of treating roadways as continuous segments, the system identifies and evaluates discrete intersections using telematics data from vehicles passing through them, allowing for precise intersection-specific risk assessment while maintaining manageable system complexity through modular data collection and processing.
Solution Approach 2:
The patent introduces telematics devices as intermediaries between vehicles and the risk assessment system. These devices collect and transmit data about vehicle behavior, location, and events at intersections, serving as a bridge that enables accurate intersection-level measurement without requiring direct complex sensing infrastructure at each intersection point.
2Measurement precision
If telematics data from multiple vehicles is collected and processed, then the accuracy of collision probability prediction improves, but the data processing complexity and computational requirements increase
Solution Approach 1:
The patent merges data from multiple telematics devices and vehicles into a centralized processing system. By combining individual vehicle data points into aggregate intersection-level statistics and patterns, the system achieves high prediction accuracy through large data volumes while managing complexity through data aggregation and consolidation at a central server rather than distributed processing.
Solution Approach 2:
The patent creates simplified representations or models of complex vehicle behavior patterns based on telematics data. Instead of processing every raw data point from each vehicle, the system generates aggregated metrics and probability models that capture essential risk patterns, reducing computational complexity while maintaining prediction accuracy through statistical modeling.
Data Source
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AI summary
Disclosed herein are systems and methods for predicting collision risk associated with a roadway intersection. The methods may comprise operating at least one processor to: receive map data and telematics data originating from telematics devices installed in a plurality of vehicles; identify, using the map data, one or more roadway intersections; determine, using the telematics data and/or map data, for each of the one or more roadway intersections, one or more roadway intersection metrics thereof; determine a hazard rating for each roadway of each roadway intersection; and generate a collision probability for each roadway intersection by inputting into a machine learning model the one or more roadway intersection metrics and the hazard rating of each roadway thereof, the collision probability representing a risk of collision for a vehicle traversing the intersection.