Grid-Based Vehicle Route Prediction Database
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current vehicle mapping technologies are static and lack coverage in areas with incomplete or dynamic environments, such as new roads or construction zones, making it difficult for driving control units to accurately predict vehicle routes and estimate route lengths without up-to-date map data.
Innovation Solution
A method and control unit that establish a grid-based representation of the geographical landscape, determining the vehicle's position and direction at cell borders, and storing these directions in a database to enable dynamic route prediction and length estimation, using a self-learning map system that updates continuously.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If static maps are used for route prediction, then map data is available and consistent, but coverage is limited in areas with new roads or dynamic environments
Solution Approach 1:
The patent transforms static map data into a dynamic system by continuously updating the database with real-time vehicle position and direction information. The grid-based representation allows the map to adapt to new roads and changing environments while maintaining structural consistency, resolving the contradiction between map coverage adaptability and route prediction reliability.
Solution Approach 2:
The system implements feedback by using detected vehicle position and direction to continuously update the database. This feedback loop allows the map to learn from actual vehicle trajectories and improve coverage in dynamic environments while maintaining reliable route predictions through accumulated statistical data.
2Measurement precision
If detailed map data is stored for all possible routes, then route prediction accuracy improves, but memory usage increases
Solution Approach 1:
The patent segments the geographical landscape into a grid-based representation where only relevant cell border information is stored. Instead of storing complete route data for all possible paths, the system divides the environment into discrete cells and stores only the directional information at cell borders, dramatically reducing memory requirements while maintaining prediction accuracy through statistical aggregation.
Solution Approach 2:
The system changes the parameter representation from storing complete route geometries to storing directional statistics at grid cell borders. This parameter transformation reduces data quantity while preserving essential route information, allowing accurate predictions with minimal memory usage through efficient data compression.
3Measurement precision
If comprehensive route data is collected and stored, then route prediction becomes more accurate, but system complexity increases
Solution Approach 1:
The patent simplifies system complexity by segmenting the geographical space into a regular grid structure. This segmentation provides a systematic framework for data collection and processing, reducing the complexity of managing comprehensive route data while maintaining prediction accuracy through structured organization of directional information at cell borders.
Data Source
Figure 1
Figure 2A
Figure 2B~2C
AI summary
Methods (400, 600) and control unit (300) for building a database (320) and for predicting a route of a vehicle (100), and estimating length of the predicted route. The method (600) comprises determining (601 ) geographical position of the vehicle (100); detecting (602) a cell border (222A) of a cell (222) in a grid-based representation (200) of a landscape, in a database (320), corresponding to the geographical position; determining (603) that the vehicle (100) is entering the cell (222) at the cell border (222A); extracting (604) a stored driving direction at the cell border (222A) from the database (320); detecting (605) a cell border (232B) of a neighbour cell (232), in the driving direction at the cell border (222A); repeating (606) step (604) and (605); predicting (607) the route of the vehicle (100); and estimating (608) the length of the predicted (607) route by adding an estimated distance through each cell (211, 212, 244) of the predicted (607) route.