Google llc (20240230354). Constrained Navigation and Route Planning simplified abstract
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This abstract first appeared for US patent application 20240230354 titled 'Constrained Navigation and Route Planning
The abstract describes a computing system that determines the most cost-effective route for traveling from a starting location to a destination, taking into account both travel costs and convenience costs associated with facilities located away from the routes.
- The system calculates travel costs and convenience costs for multiple routes connecting the starting location to the destination.
- It selects the route with the lowest combination of travel costs and convenience costs as the most optimal route.
- Route data associated with the selected route is provided to a computing device for controlling vehicle systems during navigation.
Potential Applications: - Navigation systems for vehicles - Logistics and transportation planning - Travel and tourism applications
Problems Solved: - Finding the most efficient and convenient route for travel - Optimizing travel costs and convenience factors simultaneously
Benefits: - Cost savings for travelers and transportation companies - Improved efficiency in route planning - Enhanced user experience in navigation systems
Commercial Applications: Title: "Optimized Route Planning System for Efficient Travel" This technology can be used in GPS navigation devices, ride-sharing apps, delivery services, and fleet management systems to optimize routes and reduce costs.
Prior Art: Prior research may include studies on route optimization algorithms, travel cost analysis, and convenience factors in transportation planning.
Frequently Updated Research: Researchers may be exploring advancements in real-time data integration for route planning, machine learning algorithms for predictive route optimization, and user experience enhancements in navigation systems.
Questions about Route Optimization: 1. How does the computing system calculate travel costs and convenience costs for different routes? 2. What are the potential limitations of this technology in complex urban environments with multiple variables affecting route optimization?
Original Abstract Submitted
a computing system determines, for each of a plurality of routes that respectively connect a starting location to a destination, one or more travel costs associated with travelling from the starting location to the destination, and determines, for each of the plurality of routes, one or more convenience costs associated with an availability of one or more facilities which are located away from the plurality of routes. the computing system further determines, based on the one or more travel costs and the one or more convenience costs, a first route from among the plurality of routes that is associated with a lowest combination of the one or more travel costs and the one or more convenience costs, and provides, to a computing device, route data associated with the first route for controlling one or more vehicle systems associated with navigating a vehicle.
