Distributed Map Data Processing in Vehicle Fleets
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Solution Overview
Problem
Current systems for recording and analyzing vehicle data, such as accident detection, rely on centralized solutions that do not effectively utilize on-board resources of individual vehicles to update maps in real-time, leading to inefficiencies in data processing and map updates.
Innovation Solution
A distributed data center system where individual vehicles within a fleet process and store data, using sensors and processors to generate and transmit information about their operations and surroundings, which is then used to update a centrally maintained map, leveraging transceivers for wireless communication and a remote computing server for data aggregation and map modification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If centralized solutions are used for data processing and map updates, then system management is simplified, but data processing efficiency and map update timeliness deteriorate
Solution Approach 1:
The patent divides the centralized data processing system into distributed segments by enabling individual vehicles to process and store map data locally using their onboard sensors and computing resources. Each vehicle becomes an independent data processing node that can update maps in real-time without waiting for centralized processing, thereby improving data processing efficiency while maintaining manageable system complexity through modular architecture.
Solution Approach 2:
The patent combines the computing resources, sensors, and storage capabilities of multiple individual vehicles to create a distributed data processing network. By merging these dispersed resources across the fleet, the system achieves enhanced overall processing power and real-time map update capabilities while distributing the computational load, thus improving productivity without proportionally increasing system management complexity.
2Device complexity
If centralized solutions are used for data processing and map updates, then system architecture is simplified, but map update timeliness and accuracy deteriorate
Solution Approach 1:
The patent implements preliminary action by enabling vehicles to process, validate, and store map data locally in real-time as they traverse different geographic areas. Instead of collecting all data centrally and processing it later, each vehicle performs map updates immediately during operation, significantly reducing the time delay between data generation and map incorporation while maintaining manageable architectural complexity through standardized protocols.
Solution Approach 2:
The patent establishes feedback mechanisms where vehicles continuously transmit processed map data and sensor information back to the centralized system and to other vehicles in the fleet. This real-time feedback loop enables dynamic map updates and validation, improving map timeliness and accuracy while the standardized feedback protocols keep system architecture complexity manageable through automated validation and conflict resolution algorithms.
3Productivity
If on-board resources of individual vehicles are utilized, then data processing efficiency and map update timeliness improve, but device complexity and data management challenges increase
Solution Approach 1:
The patent applies universality by designing a standardized data processing framework that can be implemented across all vehicles in the fleet using their existing onboard resources. The same sensor suites, processors, and storage systems that vehicles already possess are repurposed for map data processing, eliminating the need for specialized hardware while improving data processing efficiency. This multi-functional use of existing resources reduces data management complexity through standardization.
Solution Approach 2:
The patent changes the operational parameters of existing vehicle systems by configuring processors to execute map processing algorithms, utilizing storage systems for map data retention, and directing sensor outputs to map validation routines. By changing how existing resources are parameterized and configured rather than adding complex new hardware, the system improves data processing efficiency while keeping device and data management complexity manageable through software-based solutions.
Data Source
AI summary
This disclosure relates to a distributed data center that includes resources carried by a fleet of vehicles. The system includes sensors configured to generate output signals conveying information related to the vehicles and/or the surroundings of vehicles. The system includes a remote computing server configured to maintain map data and distribute it to the fleet, including local map data to individual vehicles pertaining to their surroundings. Individual vehicles may compare the local map data with the information related to their individual surroundings. Based on such comparisons, individual vehicles may detect discrepancies between the local map data and the information related to their individual surroundings. The remote computing server may modify and/or update the map data based on the detected discrepancies.