Task Execution Engine for Autonomous Transport Coordination
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Solution Overview
Problem
Modern life is characterized by busy schedules, leading to repetitive and time-consuming errands that often require predefined timing and location parameters, with individuals lacking assistance for these tasks.
Innovation Solution
A machine learning-based task management platform coordinates with third-party providers to optimize task execution, utilizing autonomous vehicles for transporting subjects between locations, establishing a negotiated task coordination plan that accounts for user and provider constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If autonomous vehicles are used to transport subjects, then user time commitment is reduced, but system complexity increases
Solution Approach 1:
A task coordination plan is introduced as an intermediary mechanism that mediates between the user's transport needs and the autonomous vehicle's operation. The plan coordinates pickup locations, dropoff locations, and timing without requiring direct user involvement in vehicle control, thus reducing user time commitment while managing system complexity through structured coordination
Solution Approach 2:
The autonomous vehicle performs the transport task independently without human intervention during execution. The vehicle autonomously navigates from pickup to dropoff location, eliminating the need for user time commitment during the actual transport while the system manages complexity through automated vehicle operations
2Productivity
If task coordination plans are negotiated with third party providers, then task execution is optimized, but coordination complexity increases
Solution Approach 1:
Task coordination plans are negotiated and established in advance before the actual transport task execution. By pre-coordinating pickup locations, dropoff locations, and timing with third party providers, the system optimizes task execution efficiency while managing coordination complexity through upfront planning rather than real-time negotiations
Solution Approach 2:
The coordination process is segmented into distinct components: task identification, provider selection, plan negotiation, and execution. This segmentation allows the system to optimize each phase independently, improving overall task execution efficiency while making the coordination complexity more manageable through modular processing
3Measurement precision
If machine learning is used to optimize task execution, then task completion accuracy improves, but computational requirements increase
Solution Approach 1:
Machine learning models are trained in advance on historical task data to learn optimal task execution patterns. During actual task execution, the pre-trained models provide accurate predictions with minimal computational overhead, thus improving task completion accuracy while reducing real-time computational resource requirements
Data Source
AI summary
Methods are disclosed for optimizing execution of tasks for users. A processing system including a processor detects a triggering condition for a transport task for transporting a subject from a first location to a second location via usage of an autonomous vehicle, establishes a negotiated task coordination plan between a user and a third party provider for performing the transport task, and executes the negotiated task coordination plan, wherein the executing causes the autonomous vehicle to travel between the first location and the second location for transporting the subject.