Autonomous Vehicle Fleet Scheduling for Dynamic Mission Allocation
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Solution Overview
Problem
The management of autonomous vehicle fleets is challenged by scarce resources, competing requests from geographically distributed stakeholders, and dynamically changing conditions, leading to inefficiencies in scheduling and resource allocation.
Innovation Solution
A method and system that utilize priority and rules engines to prioritize mission requests, assign vehicles and personnel based on capabilities and qualifications, and generate software/map data for vehicles, ensuring efficient scheduling and preparation for missions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual scheduling methods are used for autonomous vehicle fleets, then flexibility in handling individual requests may be maintained, but scheduling efficiency and resource utilization deteriorate due to the complexity of managing scarce AV resources and competing requests from geographically distributed stakeholders
Solution Approach 1:
The patent replaces manual scheduling mechanisms with an automated scheduling system that uses algorithms to process mission requests, assign vehicles, and optimize resource allocation. This substitution of mechanical/manual processes with automated computational systems directly addresses the contradiction by improving scheduling efficiency while managing the complexity of coordinating scarce autonomous vehicle resources across multiple stakeholders and geographic locations.
2Productivity
If automated scheduling systems are implemented to improve scheduling efficiency, then resource utilization improves, but adaptability to dynamically changing conditions (e.g., inclement weather, vehicle breakdowns) may deteriorate
Solution Approach 1:
The scheduling system is designed to be dynamic rather than static, allowing it to adapt to changing conditions. The system can reassign vehicles, adjust schedules, and respond to events such as inclement weather or vehicle breakdowns by recalculating optimal assignments based on current fleet availability and mission priorities, thus maintaining both high resource utilization and adaptability to dynamic conditions.
Solution Approach 2:
The system incorporates feedback mechanisms that monitor fleet status, mission progress, and external conditions in real-time. This feedback enables the automated scheduling system to detect changes such as vehicle breakdowns or weather events and adjust assignments accordingly, resolving the contradiction between automated efficiency and adaptability to changing conditions.
3Reliability
If comprehensive vehicle preparation (software and map data generation) is performed for each mission, then mission readiness and reliability improve, but preparation time and system complexity increase
Solution Approach 1:
The system performs preliminary generation of software and map data for vehicles before missions commence. By preparing these critical mission components in advance, the system ensures vehicle readiness and reliability while reducing last-minute preparation time. This preliminary action allows the fleet to be pre-configured for upcoming missions, balancing thorough preparation with efficient timing.
Data Source
AI summary
This document discloses system, method, and computer program product embodiments for preparing a fleet of vehicles for service. For example, the method includes receiving: a set of mission types, a set of mission requests, vehicle information related to vehicles of a fleet, and personnel information. The method further includes applying the set of mission types and the set of mission requests to one or more priority engines to produce a prioritized list of mission requests. The method further includes applying the received vehicle information, the received personnel information, and the prioritized list of mission requests to one or more rules engines to produce a proposed schedule of missions for a period of time, generating software and/or map data for one or more of the vehicles, and providing the generated software and/or map data to the vehicles prior to the period of time.