Dynamic Risk Map Generation for Route-Specific Insurance Pricing
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Solution Overview
Problem
Current GPS devices and navigation systems lack the capability to effectively determine the risk level of routes and associated insurance costs, as they primarily rely on location information without considering accident history and environmental factors, making it difficult for insurance providers to accurately assess and manage insurance premiums based on route-specific risks.
Innovation Solution
A computing system that generates a risk map by combining accident, geographic, road characteristic, environmental, and vehicle information from sensors and servers, calculating risk scores for road segments and routes, and providing alerts and insurance premium adjustments based on these scores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If GPS devices provide only location information without accident history and environmental factors, then the device complexity is reduced, but the measurement precision of route risk assessment deteriorates
Solution Approach 1:
The patent combines multiple data sources including GPS location information, accident history data, road characteristic information, and environmental factors into a unified risk assessment system. This merging of previously separate information streams enables comprehensive route risk evaluation while maintaining manageable system complexity through integrated processing.
Solution Approach 2:
The system serves multiple functions: it provides navigation guidance, assesses route risk levels, generates insurance premium recommendations, and offers safety alerts. By making the system multi-functional, it justifies the increased complexity through delivering diverse value-added services from a single integrated platform.
2Measurement precision
If the system combines multiple data sources including accident history and environmental factors, then the measurement precision of risk assessment is improved, but the device complexity increases
Solution Approach 1:
The patent segments the risk assessment process into distinct modules: data collection from multiple sources, data processing and analysis, risk score calculation, and output generation. This segmentation allows each component to be optimized independently, managing overall system complexity while maintaining high measurement precision through specialized processing at each stage.
Solution Approach 2:
The system introduces intermediary processing layers that transform raw data from multiple sources into standardized formats before analysis. These intermediaries include data normalization modules and feature extraction components that bridge the gap between diverse input sources and the risk assessment engine, reducing complexity in the core processing logic.
3Ease of operation
If insurance providers use basic location information for premium calculation, then the ease of operation is improved, but the reliability of insurance pricing deteriorates
Solution Approach 1:
The system performs preliminary risk assessment by analyzing accident history and road characteristics before the actual trip occurs. This advance analysis allows insurance providers to establish baseline risk levels and premium rates in advance, maintaining ease of operation during policy issuance while improving reliability through thorough pre-assessment of risk factors.
Solution Approach 2:
The system implements feedback mechanisms where actual trip data and incident reports are fed back into the risk assessment model. This continuous feedback loop allows the system to refine its pricing accuracy over time while maintaining ease of operation through automated updates, eliminating the need for manual reassessment.
Data Source
AI summary
A system including a computing device may receive base map information, including attribute information associated with a plurality of road segments, and trip request information. Based on this information, a route for the user to travel may be determined. The system might further calculate a risk score for each road segment forming the route, and generate a risk map based on the risk score and the route. The risk map may then be displayed to a user. The risk map may include markers or other objects depicting potential risks along the route the driver may face. Also, the risk map may be updated based on information collected from a sensor coupled to the vehicle or located at the road segment to reflect actual, real-time risk scores calculated using an equation for providing a risk score for a particular driver driving a particular vehicle on a particular road segment.