Autonomous Fleet Control With Predictive Vehicle Prepositioning
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Solution Overview
Problem
The increasing complexity and cost of autonomous vehicle fleets require centralized management systems to optimize vehicle use, maintain safety, and manage maintenance effectively, while existing solutions lack efficient demand anticipation and vehicle control mechanisms.
Innovation Solution
A fleet management system that includes a fleet controller and communications network, enabling real-time monitoring and control of autonomous vehicles through a master schedule, predictive analytics for demand anticipation, and role-based access to ensure safe and efficient operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If centralized fleet management systems are implemented to control autonomous vehicles, then vehicle deployment optimization and safety maintenance are improved, but system complexity and operational costs increase
Solution Approach 1:
The fleet management system is divided into distinct functional modules: a scheduling component that generates master schedules, a fleet controller that manages individual vehicle missions, and vehicle interfaces. This segmentation allows each component to be optimized independently while maintaining overall system coordination, reducing the complexity burden of centralized management.
Solution Approach 2:
The system generates master schedules in advance that pre-coordinate vehicle missions, resource allocation, and maintenance timing. By performing scheduling actions before actual vehicle operations begin, the system optimizes deployment efficiency without requiring complex real-time decision-making, thereby reducing operational complexity.
2Reliability
If real-time monitoring and control mechanisms are implemented for autonomous vehicles, then safety standards are maintained, but communication requirements and system complexity increase
Solution Approach 1:
The fleet controller maintains persistent connections with vehicles throughout mission execution, enabling continuous monitoring of vehicle status, location, and operational parameters. This continuous communication ensures safety compliance without requiring complex intermittent check-in protocols, as the persistent connection provides uninterrupted oversight.
Solution Approach 2:
The system implements bidirectional communication where vehicles report status information to the fleet controller, which then provides corrective commands when necessary. This feedback loop enables proactive safety management by detecting and addressing issues before they compromise mission safety, reducing the need for complex preventive communication protocols.
3Loss of time
If predictive analytics are used to anticipate demand and preposition vehicles, then wait times are reduced and operational efficiency is improved, but data processing requirements and system complexity increase
Solution Approach 1:
The scheduling component uses predictive analytics to forecast future demand patterns and generates master schedules that preposition vehicles at optimal locations before demand occurs. By performing demand anticipation and vehicle positioning in advance, the system eliminates customer wait times without requiring complex real-time data processing during peak demand periods.
Solution Approach 2:
The system dynamically adjusts vehicle deployment based on predicted demand variations, modifying master schedules to allocate vehicles to high-demand areas proactively. This dynamic scheduling approach reduces wait times by adapting to changing conditions while using historical data patterns to simplify the complexity of real-time demand prediction.
Data Source
AI summary
An method for controlling an autonomous vehicle fleet, including obtaining, by a fleet controller, from a master schedule, a mission for a vehicle of a fleet of autonomous vehicles, where the mission is associated with a mission entry of the master schedule, generating vehicle commands according to mission parameters associated with the mission, maintaining a persistent connection with the vehicle, sending the vehicle commands to the vehicle using the connection, the vehicle commands causing the vehicle to execute the mission under control of the fleet controller, and monitoring operation of the vehicle during performance of the mission.