Route Likelihood Prediction for Destination-Free Navigation
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Solution Overview
Problem
Existing digital map navigation systems fail to effectively utilize dynamic data for route guidance and driver assistance unless a specific destination is selected, as they do not efficiently determine the likelihood of a user traversing various paths within a road network.
Innovation Solution
A method and apparatus that determine the probability of a user traversing road segments based on location, direction, and historical data, using a processor to calculate probabilities for available road segments and provide navigational assistance or semi-autonomous driving by identifying the most probable paths and storing data related to road segments with probabilities satisfying a predetermined threshold.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If digital map navigation systems store copious amounts of road network data and dynamic information, then the system has comprehensive information available for route guidance, but the data cannot be effectively utilized unless a specific destination is selected and route guidance is planned
Solution Approach 1:
The system performs preliminary route probability calculations for multiple potential destinations before the user makes a selection. By pre-computing traversal probabilities for various paths in the road network based on historical probe data and current conditions, the system prepares actionable insights in advance, enabling effective data utilization immediately when a destination is selected or when providing general navigation assistance.
2Measurement precision
If the system calculates traversal probabilities for all possible paths in the road network, then the system can provide accurate navigation assistance, but the computational complexity and processing requirements increase significantly
Solution Approach 1:
The system focuses computational resources on calculating traversal probabilities for local road segments and paths that are most relevant to the user's current location and potential destinations. Rather than uniformly processing the entire road network, the system identifies and prioritizes calculations for nearby intersections and routes with higher likelihood of being traversed, based on the user's current position and historical behavior patterns.
Solution Approach 2:
The road network is divided into discrete road segments with calculated traversal probabilities. The system processes the network in manageable segments rather than as a monolithic structure, allowing parallel computation and reducing overall complexity. Each segment's probability is independently calculated based on local characteristics and historical data, then combined to form complete route probabilities.
3Adaptability or versatility
If the system provides dynamic route guidance and driver assistance without requiring a specific destination selection, then the system enhances situational awareness and navigation assistance, but existing systems fail to efficiently determine the likelihood of user traversal for various paths
Solution Approach 1:
The system uses historical probe data from multiple vehicles to automatically learn and update traversal probabilities for road segments without requiring explicit user input or destination selection. The system serves itself by continuously improving its route prediction accuracy through aggregated anonymous data, enabling it to provide adaptive navigation assistance and situational awareness efficiently even when no specific destination has been chosen.
Data Source
AI summary
Provided herein is a method for establishing the likelihood of a user traversing each of a plurality of paths through a network of roads. Methods may include: determining a location and direction of travel within a road network; determining a first road segment corresponding to the determined location and direction; determining a first set of available road segments at an end of the first road segment corresponding to the determined location in the direction of the determined direction of travel, where each available road segment of the first set has a first end proximate the end of the first road segment and a second end, between which the respective road segment extends; and calculating a probability for each available road segment of the first set of available road segments at the end of the first road segment.