Vehicle Navigation Using Route-Specific Accident Risk Modeling
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Solution Overview
Problem
Insurance companies face challenges in accurately determining and forecasting accident risk associated with evolving transportation technologies, including autonomous and semi-autonomous vehicles, due to the lack of comprehensive and real-time analysis of sensor and environmental data, making it difficult to set appropriate insurance premiums.
Innovation Solution
A navigation system utilizing a modeling computing device that collects sensor and environmental data to generate an accident risk model using machine learning models, allowing for real-time analysis and adjustment of insurance premiums based on vehicle and route-specific risk factors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If comprehensive sensor data collection from multiple vehicles is implemented, then accident risk assessment accuracy is improved, but system complexity and data processing requirements increase
Solution Approach 1:
The system segments the complex task of accident risk assessment by dividing it into multiple independent machine learning models, each responsible for analyzing specific aspects of sensor data from different vehicles. This allows the system to process comprehensive data without overwhelming complexity by handling analysis in modular, manageable components.
Solution Approach 2:
The patent introduces computing devices as intermediaries that collect, aggregate, and process sensor data from multiple vehicles before presenting it for accident risk analysis. These intermediary computing systems manage the complexity of data collection and preprocessing, enabling accurate risk assessment without directly burdening the core analysis system with raw data management complexity.
2Measurement precision
If real-time accident risk modeling is performed using machine learning, then insurance pricing accuracy is improved, but computational resources and processing time increase
Solution Approach 1:
The system performs preliminary actions by pre-processing sensor data and pre-training machine learning models with historical accident data before real-time risk assessment is needed. This allows the computationally intensive work to be done in advance, reducing the computational resources required during actual real-time insurance pricing while maintaining high accuracy.
Solution Approach 2:
The patent implements partial action by focusing machine learning analysis on only the most relevant features and parameters for accident risk prediction, rather than processing all possible sensor data equally. This selective approach maintains pricing accuracy while significantly reducing the computational resources and energy required for real-time modeling.
3Reliability
If detailed route-specific risk factors are analyzed, then navigation safety is improved, but data processing complexity increases
Solution Approach 1:
The system applies local quality by analyzing risk factors specifically for each route segment rather than treating all navigation paths uniformly. Machine learning models evaluate sensor data and environmental conditions locally for each geographic area and route characteristic, providing tailored safety assessments that improve navigation reliability without requiring globally complex processing for every possible scenario.
Data Source
AI summary
In one aspect, an example method includes: (a) collecting sensor data from one or more vehicles operating within a geographic region and environmental data for the geographic region; (b) generating an accident risk model using one or more machine learning models; (c) receiving a request for navigating between a first geographic position and a second geographic position; (d) identifying attributes of one or more routes between the first geographic position and the second geographic position and through the geographic region, wherein the identified attributes of the one or more routes are based on at least the generated accident risk model; and (e) transmitting an instruction that causes a mobile computing device to display a graphical indication of: (i) the one or more routes; and (ii) the identified attributes of the one or more routes.