Semantic Map Grouping for Delivery Route Optimization
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Solution Overview
Problem
Current GPS-based navigation systems for delivery services lack accuracy, particularly in dense physical structures, and fail to account for real-time updates, temporary road closures, construction zones, and traffic congestion, leading to inefficient route optimization.
Innovation Solution
A computer-implemented method that groups delivery addresses based on semantic connections, using detailed maps created from individual inspections and local data, to optimize routes and provide navigation through dense areas, including convenient parking, staircases, and access codes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If GPS-based navigation systems use standard satellite maps, then the system is easy to operate and widely available, but the navigation accuracy and detail level deteriorates in dense physical structures
Solution Approach 1:
The patent segments the mapping approach into two layers: standard satellite maps for general navigation and custom-generated detailed maps for dense physical structures. This allows the system to use simple GPS maps for open areas while switching to detailed semantic maps when entering complex environments like apartment complexes or business parks, thereby resolving the contradiction between ease of operation and navigation accuracy.
Solution Approach 2:
The patent introduces an intermediary component - a detailed mapping system that captures semantic information about dense physical structures. This intermediary layer sits between the standard GPS map and the navigation instructions, providing enhanced detail about parking locations, building entrances, staircases, and access points without requiring the entire GPS system to be complex.
2Measurement precision
If detailed maps of dense physical structures are created and displayed, then navigation accuracy improves, but the complexity of the map display system increases
Solution Approach 1:
The patent implements dynamic map display that adapts to the user's location and context. The system automatically adjusts the level of detail shown based on whether the user is in a dense physical structure or open area, and modifies the display based on the user's current task (e.g., looking for parking vs. navigating to a destination). This dynamic adaptation reduces unnecessary complexity while maintaining high accuracy when needed.
Solution Approach 2:
The patent applies local quality by providing detailed semantic information only in specific locations where it is needed (dense physical structures) rather than uniformly across all areas. The system enhances map detail locally at apartment complexes, business parks, and subdivisions while maintaining simpler displays for open roads and rural areas, thereby reducing overall system complexity while improving navigation accuracy where required.
3Reliability
If real-time map updates are implemented to reflect road closures and traffic, then route reliability improves, but the system complexity and data processing requirements increase
Solution Approach 1:
The patent applies preliminary action by pre-capturing detailed semantic information about dense physical structures during off-peak times through individual inspections and local data collection. This pre-collected data is stored and ready for use, eliminating the need for complex real-time processing during actual navigation. The system updates routes by matching current GPS data against this pre-established detailed map framework, reducing real-time processing complexity while maintaining high reliability.
4Productivity
If semantic connections between addresses are used to group deliveries, then productivity increases, but the complexity of route optimization algorithms increases
Solution Approach 1:
The patent adds a semantic dimension to traditional geographic route optimization. Instead of only considering physical distance and travel time, the system groups deliveries based on semantic connections between addresses (e.g., addresses within the same apartment complex, business park, or subdivision). This semantic grouping creates natural clusters that simplify route planning, allowing drivers to service multiple locations efficiently within the same semantic boundary without requiring complex multi-variable optimization algorithms.
Data Source
AI summary
In some embodiments, a computer-based, pickup and delivery system is configured to perform a pickup/delivery route optimization and dispatch optimized route information to client applications. The optimization may include optimizing the routes for servicing multiple delivery and/or pickup stops. The optimization may also include optimizing the route navigation along the routes from one location to another. The optimized navigation may include generating a destination mapping throughout, for example, an apartment complex or a business park, and determining conveniently located parking to be used to park delivery vehicles, conveniently located staircases, conveniently located elevators, access codes to the secured gates and lockboxes, and the like.