Vehicle Guidance Using Systemic Utility for Network-Wide Routing
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Solution Overview
Problem
Current vehicle guidance systems fail to optimize vehicle operations in transportation networks due to incomplete or improperly weighted operational data, leading to sub-optimal systemic utility, where the operational utility of individual vehicles does not account for all relevant information affecting the entire region, resulting in inefficiencies and potential safety risks.
Innovation Solution
A method for vehicle guidance with systemic optimization, which involves obtaining and utilizing comprehensive vehicle operational and environmental data to operate a systemic-utility vehicle guidance model, generating systemic-utility vehicle guidance data that balances operational costs such as risk and travel time across the region, and providing this data to vehicles to optimize their routes and operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If individual vehicles optimize their operations based on limited local data, then each vehicle achieves its own operational utility, but the overall systemic utility of the transportation network remains sub-optimal
Solution Approach 1:
The patent combines operational data from multiple vehicles and a centralized server to create a comprehensive systemic utility model. Individual vehicle data is merged with network-wide information to generate optimized guidance that benefits the entire transportation system rather than just individual vehicles.
Solution Approach 2:
A centralized server acts as an intermediary that collects data from multiple vehicles, processes it through a systemic utility model, and distributes optimized guidance back to vehicles. This intermediary enables individual vehicles to access network-wide information without direct peer-to-peer communication.
2Reliability
If comprehensive vehicle operational data is collected and processed across the region, then systemic utility is optimized, but the complexity of the guidance system increases
Solution Approach 1:
The system is segmented into distinct functional components: data collection modules in vehicles, a centralized processing server, and guidance distribution systems. This segmentation allows complex operations to be divided into manageable tasks performed at different locations in the network.
Solution Approach 2:
Vehicles autonomously collect and transmit their own operational data to the centralized server, and receive optimized guidance instructions. Each vehicle serves itself by participating in the data collection process, reducing the need for external monitoring infrastructure.
3Productivity
If vehicles use discrete control operations to maximize predicted operational utility, then individual vehicle performance improves, but network-wide inefficiencies and safety risks persist
Solution Approach 1:
The system implements continuous feedback loops where vehicle operational data is collected, processed through the systemic utility model, and used to generate optimized guidance instructions that are fed back to vehicles. This feedback mechanism enables real-time adjustments to prevent safety issues and optimize network-wide performance.
Solution Approach 2:
The centralized server processes operational data and generates optimized guidance instructions in advance, before vehicles encounter potential safety issues or inefficiencies. This preliminary processing allows proactive optimization rather than reactive response to problems.
Data Source
AI summary
Vehicle guidance with systemic optimization may include traversing, by a current vehicle, a vehicle transportation network, by obtaining, by the current vehicle, systemic-utility vehicle guidance data for a current portion of the vehicle transportation network and traversing, by the current vehicle, the current portion of the vehicle transportation network in accordance with the systemic-utility vehicle guidance data. Obtaining the systemic-utility vehicle guidance data may include obtaining vehicle operational data for a region of a vehicle transportation network, wherein the vehicle operational data includes current operational data for a plurality of vehicles operating in the region, operating a systemic-utility vehicle guidance model for the region, obtaining systemic-utility vehicle guidance data for the region from the systemic-utility vehicle guidance model in response to the vehicle operational data, and outputting the systemic-utility vehicle guidance data to the current vehicle.