Elastic In-Vehicle Computing for Local-Remote Task Allocation
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Solution Overview
Problem
Existing in-vehicle computing systems face challenges in balancing local and remote processing loads, leading to inefficient resource allocation and potential need for additional computing capacity.
Innovation Solution
The implementation of an elastic computing module that dynamically allocates local and remote hardware computing resources based on processing needs, determining computing and memory capacities, and partitioning software applications across domains to optimize resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If remote computing resources are used to provide sufficient processing capacity, then computing power is improved, but system complexity and cost increase
Solution Approach 1:
The patent implements dynamic allocation of computing tasks between local and remote processing resources based on real-time vehicle conditions, software demands, and resource availability. The system continuously adjusts the distribution of computational loads, transitioning tasks between local vehicle computers and remote cloud platforms as needed, thereby optimizing computing power utilization while managing system complexity adaptively
Solution Approach 2:
The patent segments computing tasks into different categories based on safety requirements, performance needs, and resource availability. Critical safety-related functions are processed locally on vehicle computers, while non-critical functions are offloaded to remote computing resources. This segmentation allows the system to leverage remote computing power without proportionally increasing overall system complexity
2Speed
If local computing resources are increased to handle all processing needs, then processing speed is improved, but hardware cost and complexity increase
Solution Approach 1:
The patent introduces an elastic computing module as an intermediary that manages the distribution of computing tasks between local vehicle computers and remote computing platforms. This intermediary component coordinates task allocation, monitors resource availability, and dynamically adjusts processing distribution, enabling the system to achieve high processing speeds for critical functions while avoiding the need for excessively complex local hardware configurations
Solution Approach 2:
The system dynamically adjusts the balance between local and remote processing based on real-time conditions. When local resources are sufficient and responsive, tasks are processed locally to maintain high speed. When additional capacity is needed or local resources are constrained, the elastic computing module offloads tasks to remote platforms, thereby maintaining processing speed requirements without permanently increasing local hardware complexity
3Device complexity
If remote processing is used to reduce local hardware requirements, then device complexity is reduced, but processing latency increases
Solution Approach 1:
The patent applies local quality by processing different types of tasks at different locations based on their specific requirements. Time-critical, safety-related functions are processed locally on vehicle computers to minimize latency, while non-time-critical functions are processed remotely. This differentiated approach ensures that local hardware complexity is reduced for non-critical functions while maintaining low latency for critical operations through local processing
4Stability of the object's composition
If computing resources are statically allocated, then system stability is improved, but resource utilization efficiency decreases
Solution Approach 1:
The patent implements dynamic resource allocation through the elastic computing module, which continuously monitors computing demands, resource availability, and system conditions. Based on this monitoring, the module dynamically adjusts task allocation between local and remote resources, ensuring stable system operation while optimizing resource utilization efficiency. The system maintains stability through controlled adaptation rather than rigid static allocation
Data Source
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AI summary
Examples are disclosed for in-vehicle systems and methods of allocating local and remote hardware computing resources. An example 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.