Fleet Route Planning via Centralized Road Weight Preprocessing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing route planning techniques for vehicles, especially in complex and dynamic environments, face challenges in efficiently determining safe and optimal driving routes due to the need for processing large amounts of raw traffic and road condition data on-board, which overloads computational resources and lacks centralized control.
Innovation Solution
A remote system, such as a fleet management system, preprocesses traffic, road, and fleet data to generate optimized road weights that reflect expected travel times, reducing data transmission and computational load on vehicles by transmitting these weights instead of raw data, allowing for decentralized route planning and dynamic adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If vehicles process large amounts of raw traffic and road condition data on-board, then route planning accuracy is improved, but computational resource overload occurs
Solution Approach 1:
The patent extracts the heavy computational task of processing raw traffic and road condition data from individual vehicles and centralizes it in a remote server system. The server receives raw data from multiple vehicles, processes it centrally to generate optimized route weights, and transmits only the essential weight parameters back to vehicles. This extraction resolves the contradiction by maintaining route planning accuracy through centralized processing while eliminating on-vehicle computational resource overload.
Solution Approach 2:
The patent transforms the route planning problem from a high-dimensional raw data processing task into a lower-dimensional weight-based decision problem. Instead of vehicles processing complex multi-source raw data (traffic flow, road conditions, weather, etc.), the system converts all this information into simplified weight parameters that represent route desirability. This dimensional reduction allows vehicles to make accurate routing decisions with minimal computational resources.
2Measurement precision
If raw data is transmitted to each vehicle, then route planning precision is improved, but data transmission load increases
Solution Approach 1:
The patent extracts only the essential routing information from the complete raw data set and transmits only this condensed information to vehicles. The remote server processes all raw traffic and road condition data, then extracts and transmits only the optimized weight parameters needed for route planning. This extraction maintains route planning precision while dramatically reducing data transmission volume compared to sending complete raw data sets to each vehicle.
Solution Approach 2:
The patent transforms voluminous raw data into compact weight parameters, changing the data representation from high-volume raw measurements to low-volume processed weights. This dimensional transformation preserves the essential routing information needed for precise route planning while reducing data transmission requirements by orders of magnitude.
3Productivity
If centralized route planning is implemented, then fleet-wide optimization is improved, but system complexity increases
Solution Approach 1:
The patent merges the route planning functions of individual vehicles into a centralized remote server system. By combining data collection, processing, and route optimization into a single centralized system, the patent achieves fleet-wide optimization through coordinated routing decisions. The server considers fleet-wide conditions and generates optimized routes for multiple vehicles simultaneously, improving overall fleet productivity while managing system complexity through centralized architecture.
Solution Approach 2:
The remote server system performs multiple functions: collecting data from multiple vehicles, processing traffic and road condition information, generating optimized route weights, and distributing routing instructions to vehicles. This multi-functional universal system achieves fleet-wide optimization while consolidating complexity into a single platform that handles all routing operations for the entire fleet.
4Adaptability or versatility
If on-vehicle route planning is used, then vehicle independence is improved, but adaptability to dynamic conditions worsens
Solution Approach 1:
The remote server performs preliminary processing of traffic and road condition data before transmitting routing information to vehicles. By pre-calculating optimized route weights based on current dynamic conditions and transmitting them to vehicles in advance, the system enables vehicles to independently follow optimized routes without real-time computational delays. This preliminary action maintains vehicle independence while improving responsiveness to dynamic conditions through pre-processed routing guidance.
Solution Approach 2:
The system implements feedback by continuously receiving data from vehicles about their locations and conditions, processing this information centrally, and updating route weights accordingly. This feedback loop allows the centralized system to adapt to changing dynamic conditions and communicate updated routing instructions to vehicles, maintaining both vehicle independence through received guidance and high adaptability through continuous central monitoring and adjustment.
Data Source
AI summary
The techniques described herein relate to controlling and/or influencing the routes driven by vehicles, autonomous or otherwise, such as vehicles in a fleet of vehicles managed by a fleet management system. In some cases, the techniques described herein relate to centrally generating road weights using a remote system such as a fleet management system and providing those pre-calculated weights to vehicles to simplify onboard route planning. Rather than transmitting large amounts of raw traffic, road condition, and other data to each vehicle, the remote system pre-processes the data into condensed road weights optimized for route planning. This architecture provides various technical advantages such as reduced data transmission, decreased computational load on vehicles, and decentralized control of fleet-wide routing.