In-Vehicle Elastic Computing for Balanced Local-Remote Processing
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Solution Overview
Problem
In-vehicle computing systems face challenges with unbalanced local and remote processing loads and the need for additional computing capacity, particularly when sufficient resources are available locally.
Innovation Solution
Implementing an elastic computing module that dynamically allocates computing resources between local and remote devices based on capacity and software demands, using domain-based partitioning and elastic computing to optimize resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If remote cloud computing resources are used to provide sufficient processing and memory resources, then computing capacity is improved, but system complexity and cost increase
Solution Approach 1:
The patent segments computing tasks into local and remote portions, with the head unit handling local processing and the remote server handling cloud-based processing. This segmentation allows the system to leverage remote computing resources for capacity while maintaining simpler local device architecture.
Solution Approach 2:
The head unit acts as an intermediary between the vehicle systems and the remote cloud server. It manages the distribution of computing tasks, coordinating between local processing capabilities and remote cloud resources, thereby reducing the complexity burden on any single component.
2Power
If remote cloud processing is used, then additional computing capacity is available, but processing load becomes unbalanced between local and remote systems
Solution Approach 1:
The system dynamically balances the processing load between the head unit and remote server based on real-time conditions. The head unit can adjust which tasks are processed locally versus remotely, optimizing processing efficiency by matching task requirements with available computing resources at either location.
Solution Approach 2:
The system changes the parameter of task distribution dynamically, adjusting the proportion of computing tasks executed locally versus remotely based on system state, task characteristics, and resource availability, thereby maintaining optimal processing efficiency.
3Adaptability or versatility
If complex processing and memory resources are included in the head unit, then functionality is improved, but device cost and complexity increase
Solution Approach 1:
The patent extracts complex processing and memory resources from the head unit and relocates them to a remote cloud server. This extraction maintains the functionality required for sophisticated multimedia and vehicle control while significantly reducing the complexity and cost of the in-vehicle head unit.
Solution Approach 2:
The remote cloud server provides universal computing resources that can serve multiple functions and applications. Instead of duplicating complex processing capabilities in the head unit, the system accesses shared remote resources that can dynamically adapt to different computational needs.
Data Source
AI summary
Options are disclosed for in-vehicle systems and methods of allocating local and remote hardware computing resources. One such system for controlling a vehicle includes a first computing device physically positioned in the vehicle, a second computing device positioned away from the vehicle, and an elastic computing module communicatively coupled to the first computing device and the second computing device, the elastic computing module configured to: determine computing and memory capacities of the first computing device and the second computing device; determine software demands of a software application for controlling a component of the vehicle; and dynamically allocate processing of the software application to the first computing device and/or the second computing device based on the computing and memory capacities of the first and second computing devices and the software demands of the software application.