Autonomous Fleet Deployment Using Resource Performance Parameters
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Solution Overview
Problem
Autonomous vehicle fleets face inefficiencies in resource management, leading to wasteful usage of computational resources due to idle time and suboptimal deployment in operational domains, which affects safety, productivity, and environmental impact.
Innovation Solution
A computer-implemented method and system that evaluates autonomous vehicle fleets by determining resource performance parameters based on vehicle capabilities and service dynamics within operational domains, optimizing fleet size and deployment to minimize idle time and enhance resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If autonomous vehicle fleets are deployed without optimized fleet size determination, then service coverage in operational domains is maintained, but computational resources are wasted due to idle time and suboptimal deployment
Solution Approach 1:
The system changes the parameter of fleet size based on dynamic evaluation of vehicle capabilities and service dynamics. By determining optimal fleet sizes through resource performance parameters, the system adjusts deployment quantities to match actual service needs, reducing computational waste from idle vehicles while maintaining adequate service coverage.
Solution Approach 2:
The system implements feedback mechanisms by evaluating vehicle capabilities and service dynamics continuously. Resource performance parameters are determined based on this feedback loop, allowing the system to adjust fleet deployment decisions dynamically. This ensures computational resources are utilized efficiently without compromising service coverage in operational domains.
2Productivity
If fleet size is increased to improve service coverage, then productivity is improved, but computational resource usage increases due to more vehicles requiring processing power
Solution Approach 1:
The system optimizes the parameter of fleet size by determining resource performance parameters that balance service coverage needs against computational resource consumption. This allows achieving maximum productivity with the minimum necessary fleet size, preventing excessive computational resource usage while maintaining adequate service coverage.
3Adaptability or versatility
If autonomous vehicles operate without optimized deployment, then operational flexibility is maintained, but resource efficiency deteriorates due to idle time
Solution Approach 1:
The system introduces dynamic optimization by determining fleet sizes based on evaluated resource performance parameters. This dynamic approach allows the fleet deployment to adapt to changing service dynamics while maintaining operational flexibility. The system balances the need for operational versatility with the requirement to minimize idle time and improve resource efficiency.
Data Source
AI summary
Systems and methods for evaluating and deploying fleets of autonomous in operational domains are described. A computing system may obtain data indicative of one or more capabilities of at least one autonomous vehicle, data indicative of vehicle service dynamics in an operational domain over a period of time, and determining a plurality of resource performance parameters respectively for a plurality of autonomous vehicle fleets associated with potential deployment in the operational domain. Each autonomous vehicle fleet can be associated with a different number of autonomous vehicles The resource performance parameter for each autonomous vehicle fleet can be based at least in part on the one or more capabilities of the at least one autonomous vehicle and the vehicle service dynamics in the operational domain. The computing system can initiate an action associated with the operational domain based at least in part on the plurality of resource performance parameters.