Vehicular Task Allocation Using External Compute Redundancy
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Solution Overview
Problem
Infrastructure assisted automated driving systems face high computational requirements, leading to high costs and energy consumption due to the need for extensive computational hardware in vehicles, which is often not utilized at peak levels, and existing solutions fail to address the reliability and safety of external computational resources during failures.
Innovation Solution
A method for planning computational task allocation from vehicle resources to external resources in a vehicular network by obtaining spatial and availability data, comparing computational requirements to resource availability, and defining valid routes for safe operation, incorporating redundancy and real-time monitoring to ensure reliable communication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If high computational hardware is installed in autonomous vehicles to meet peak computational requirements, then computational capability is improved, but unit cost and energy consumption increase
Solution Approach 1:
The patent extracts computational tasks from the vehicle's onboard hardware and relocates them to external computational resources in the infrastructure. The vehicle controller identifies atomic computational tasks and transfers them to external resources, allowing the vehicle to operate with reduced onboard computational hardware while maintaining peak computational capability when needed.
Solution Approach 2:
The patent creates a universal computational infrastructure where external resources can serve multiple vehicles and multiple computational tasks. The same external computational resources can be shared across different vehicles and different routes, providing multi-functionality that reduces the need for each vehicle to have dedicated peak-capability hardware.
2Power
If high computational hardware is installed in autonomous vehicles to meet peak computational requirements, then computational capability is improved, but device complexity increases
Solution Approach 1:
The patent extracts computational tasks from the vehicle's onboard hardware and relocates them to external computational resources in the infrastructure. The vehicle controller identifies atomic computational tasks and transfers them to external resources, allowing the vehicle to operate with reduced onboard computational hardware while maintaining peak computational capability when needed.
3Use of energy by moving object
If external computational resources are used to reduce onboard hardware requirements, then energy consumption is reduced, but reliability decreases due to potential failures of external resources
Solution Approach 1:
The patent implements beforehand cushioning by establishing redundant computational task allocation options before the vehicle begins its route. The system identifies multiple external computational resources that could potentially handle the computational tasks and pre-establishes allocation plans. This ensures that if one external resource fails, alternative resources are already identified and ready to take over, maintaining system reliability.
Solution Approach 2:
The patent changes the parameter of resource availability by continuously monitoring and updating the status of external computational resources. The vehicle controller compares computational requirements with the current availability of external resources, adjusting task allocation dynamically. This allows the system to adapt to changing conditions and maintain reliability even when external resources experience failures or varying availability.
Data Source
AI summary
A computer-implemented method for planning an allocation of at least one computational task from a computational resource comprised in at least one vehicle to one or more of a plurality of external computational resources in a vehicular communications network. The method comprises obtaining a spatial representation of a region characterising at least one route of a vehicle from a first location to a second location, and data characterising an availability of external computational resources at a plurality of locations in the region, providing at least one computational requirement indication of at least one atomic computational task required by the vehicle during a prospective movement of the vehicle from the first location to the second location, comparing the at least one computational requirement indication to the data characterising the availability of external computational resources at the plurality of locations in the region.