Vehicle Resource Optimization Module for Local Remote Data Processing
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Solution Overview
Problem
Current vehicle computing systems face inefficiencies in processing and optimizing data from sensors, as they lack effective methods to determine whether data should be processed locally or remotely based on resource availability and data importance, leading to potential delays and privacy concerns.
Innovation Solution
A resource optimization module that monitors local computing resources and sensor data, determining whether to process data locally or remotely by considering availability, importance, and constraints such as bandwidth, computing power, and data privacy, allowing for streaming, batch, or hybrid processing approaches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If all sensor data is processed locally in real-time, then response speed is improved, but computing resource consumption increases
Solution Approach 1:
The patent segments data processing into local and remote components. Critical safety-related data is processed locally in real-time using onboard computing resources, while non-critical data is processed remotely in the cloud. This segmentation allows the system to maintain fast response times for essential functions while reducing overall computing resource consumption by offloading less time-sensitive processing tasks.
2Use of energy by moving object
If data is processed remotely, then local computing resource usage is reduced, but transmission bandwidth requirements increase
Solution Approach 1:
The patent applies partial action by selectively transmitting only the necessary portion of sensor data to remote servers. Instead of transmitting all raw sensor data, the system processes critical data locally and transmits only essential information or processed results to the cloud. This approach reduces local computing resource usage while minimizing the bandwidth required for data transmission.
3Power
If critical data is transmitted for remote processing, then processing capability is improved, but data privacy and security risks increase
Solution Approach 1:
The patent implements local quality by maintaining different processing locations for different data types based on their sensitivity and requirements. Critical safety data that requires fast processing remains local, while non-sensitive data is transmitted for remote processing. This differentiated approach ensures that data privacy and security are maintained for sensitive information while still leveraging remote processing capabilities for appropriate data sets.
4Reliability
If local computing resources are allocated for all processing tasks, then processing reliability is improved, but resource efficiency deteriorates
Solution Approach 1:
The patent introduces a data prioritization mechanism as an intermediary that determines whether data should be processed locally or remotely. This intermediary evaluates data criticality, available computing resources, and transmission conditions to make intelligent routing decisions. Critical data is processed locally to ensure reliability, while non-critical data is processed remotely to improve resource efficiency, achieving a balance between both objectives.
Data Source
AI summary
This disclosure describes various embodiments for resource optimization in a vehicle. In an embodiment, a system for resource optimization in a vehicle is described. The system may comprise a memory; a processor coupled to the memory; and a resource optimization module. The resource optimization module may be configured to: monitor usage of local computing resources of the vehicle, the local computing resources comprising the processor and available bandwidth of a transmission medium; determine an availability of the local computing resources; evaluate data captured by one or more sensors of the vehicle; and determine whether to process the data locally or remotely based, at least in part, on the availability of the local computing resources and the data captured by the one or more sensors.