Dynamic Driver Neighborhood Maps for Fair Route Comparison
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Solution Overview
Problem
Drivers lack effective tools to analyze and compare their driving activities fairly, as their behaviors vary significantly based on different routes, making it difficult to improve driving scores and insurance premiums.
Innovation Solution
A system and method to generate a custom dynamic neighborhood map by aggregating geolocation and auxiliary data, identifying frequently traversed routes, and generating a focused map that excludes outliers, allowing drivers to visualize and compare their driving habits with others.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If drivers are provided with comprehensive logging tools that track all driving activities across all routes, then complete driving behavior data is captured, but the data becomes difficult to analyze fairly due to dramatic variations in driving behaviors across different routes
Solution Approach 1:
The patent segments driving activities into route-specific groups, analyzing driving behavior separately for each route rather than aggregating all routes together. This allows for fair comparison by accounting for route-specific conditions that affect driving behavior.
Solution Approach 2:
Instead of comparing all driving activities across all routes uniformly, the patent inverts the approach by grouping activities by route and comparing within each route group, thereby accounting for route-specific driving patterns and making fair comparisons possible.
2Loss of information
If all driving routes are included in the neighborhood map, then complete driving history is represented, but statistically insignificant routes (outliers) distract from the meaningful patterns
Solution Approach 1:
The patent extracts and excludes statistically insignificant routes (outliers) from the neighborhood map, keeping only the most frequently traversed routes. This removes distracting information while preserving the meaningful driving patterns that matter for safety assessment.
Solution Approach 2:
The patent applies different inclusion criteria to different routes based on their statistical significance. Frequently traversed routes are included in the neighborhood map while rare routes are excluded, creating a map with locally optimized quality for each route type.
3Ease of operation
If driving comparison is made across all routes without route-specific analysis, then simple comparison is possible, but fair representation of driving behavior is lost due to route-specific tendencies
Solution Approach 1:
The patent implements dynamic, route-specific analysis thresholds and grouping criteria that adapt to each route's characteristics. This allows the comparison system to remain simple in operation while achieving fairness through adaptive, context-aware analysis parameters.
Data Source
AI summary
A computer-implemented method for generating maps, the method comprising: receiving geolocation data and auxiliary data associated with driving activities of a user, wherein the auxiliary data comprise a set of driving behaviors that correlate to the geolocation data; training a machine learning model based on the geolocation data and the auxiliary data; identifying, using the trained machine learning model, a plurality of driving routes of the user; determining one or more driving routes of the plurality of routes that are traversed more frequently; generating a user map including the one or more routes, wherein the user map is populated with information related to one of the auxiliary data associated with the one or more routes; and transmitting the user map to a user device for display on a user interface.