Navigation Route Calculation Using Predicted User Mistake Probability
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Navigation systems face challenges in personalizing routes for individual users, as current options are limited to shortest or fastest routes, leading to potential user mistakes and increased travel distance or time, and inefficient resource usage due to recalculations.
Innovation Solution
A method and apparatus that calculate routes by predicting user mistakes at intersections, assessing the probability and severity of these mistakes, and recursively recalculating the route to minimize errors, presenting an optimal route that considers user behavior and reduces travel impact.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If navigation systems provide only shortest or fastest routes without considering user behavior, then route calculation is simple and fast, but users are more likely to make routing mistakes and require frequent recalculations
Solution Approach 1:
The system performs preliminary analysis of user routing behaviors and potential mistake locations before generating the final route. By pre-identifying intersections where users are likely to make mistakes and calculating alternative routes in advance, the system reduces the need for complex real-time recalculations when mistakes occur.
Solution Approach 2:
The system incorporates feedback loops where route calculations consider predicted user mistakes and their consequences. The recursive recalculation process uses feedback from mistake probability assessments to continuously refine route recommendations, balancing route optimality with user error tolerance.
2Reliability
If navigation systems recursively recalculate routes after user mistakes, then route accuracy is maintained, but computational resources are wasted
Solution Approach 1:
The system pre-calculates alternative routes and mistake scenarios before they are needed. By having backup routes prepared in advance based on predicted user error patterns, the system minimizes the computational burden of post-m mistake recalculations.
Solution Approach 2:
The system dynamically adjusts calculation parameters based on the severity and probability of predicted mistakes. For high-probability, high-severity mistake locations, the system performs more thorough recursive recalculations, while for low-risk locations, it uses simpler route adjustments, optimizing computational resource allocation.
3Adaptability or versatility
If navigation systems use limited personalization options (shortest/fastest routes), then system simplicity is maintained, but user needs and error tendencies are not addressed
Solution Approach 1:
The system automatically analyzes user routing behaviors and mistake patterns without requiring explicit user input or configuration. By self-learning from aggregated user data and automatically adjusting route recommendations based on predicted user errors, the system provides personalized routing while maintaining operational simplicity for users.
Solution Approach 2:
The system integrates multiple functions including traditional shortest/fastest route calculation, user behavior analysis, mistake prediction, and adaptive route recommendation within a unified framework. This multi-functionality allows the system to serve diverse user needs while managing complexity through integrated architecture.
Data Source
AI summary
An approach is provided for route calculation using a navigation system by considering potential mistakes. Using the navigation system, at least one route is calculated. A map database is queried to identify intersections along the at least one route. A predicted probability of a user making a routing mistake at the intersections is calculated. A travel time, travel distance, or a combination thereof that is predicted to result from the routing mistake is calculated. The travel time, the travel distance, or a combination thereof represents a predicted severity of the routing mistake. At least one route is recalculated to accept or avoid the intersections based on the predicted probability and the predicted severity of the routing mistake. The recalculated route is presented in a user interface as an optimal route.