Distributed Fleet Control for Predictive Autonomous Vehicle Scheduling
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Solution Overview
Problem
The increasing complexity and cost of autonomous vehicle fleets require advanced centralized management systems to optimize vehicle use, safety, and maintenance, while existing technologies struggle to anticipate demand and manage fleets efficiently.
Innovation Solution
A fleet management system that includes a fleet controller and communications network, enabling real-time monitoring and control of autonomous vehicles, predictive scheduling based on historical data and environmental factors, and anticipatory vehicle positioning to meet demand, with features like role-based access, component tracking, and predictive maintenance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If centralized fleet management systems are implemented to manage large autonomous vehicle fleets, then vehicle control and coordination capability is improved, but system complexity and cost increase
Solution Approach 1:
The fleet management system is divided into multiple distributed fleet controllers rather than a single centralized controller. Each fleet controller manages a subset of vehicles and communicates with other controllers through a communications network, distributing the computational and control burden across multiple nodes while maintaining coordinated fleet-wide management
Solution Approach 2:
The system transitions from a traditional hierarchical centralized control model to a distributed networked control architecture. By introducing a communications network dimension that enables peer-to-peer and multi-hop communications between fleet controllers, the system achieves centralized coordination capabilities without the single point of failure and bottleneck issues of traditional centralized systems
2Reliability
If real-time monitoring and control of autonomous vehicles is implemented, then safety and operational control are improved, but communication requirements and system complexity increase
Solution Approach 1:
The fleet controller maintains a persistent connection with vehicles and receives status updates proactively before issues arise. The system performs predictive scheduling and anticipatory vehicle positioning based on historical data and environmental factors, allowing preventive maintenance and demand anticipation without requiring complex real-time emergency response systems
Solution Approach 2:
The system implements continuous feedback loops where vehicles report status, location, and sensor data to the fleet controller, which processes this information and sends updated commands. The persistent connection enables bidirectional communication with automatic acknowledgment and retransmission mechanisms, ensuring reliable feedback without requiring overly complex communication protocols
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.