Autonomous Fleet Scheduling for Battery and Depot Constraints
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Solution Overview
Problem
Managing a fleet of autonomous vehicles to meet demand while avoiding issues like battery depletion and depot congestion, which can hinder the ability to meet future demand.
Innovation Solution
A method and system that use processors to identify inputs such as current and future demand, vehicle status, and depot information to determine a schedule for assigning autonomous vehicles to predefined states, thereby optimizing their behavior and resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If autonomous vehicles operate continuously to meet current demand, then service coverage is improved, but battery depletion and depot congestion occur which hinder future demand fulfillment
Solution Approach 1:
The fleet management system performs preliminary actions by predicting future vehicle states (including battery charge levels, maintenance needs, and location) before they actually occur. Based on these predictions, the system proactively schedules charging events, maintenance events, and vehicle relocations to prevent battery depletion and depot congestion before they happen, ensuring both current service coverage and future demand fulfillment capability are maintained
2Productivity
If the fleet management system monitors and schedules all vehicle states in detail, then operational efficiency is improved, but system complexity increases
Solution Approach 1:
The fleet management system employs machine learning models that automatically learn from historical and real-time data to predict vehicle states without requiring complex manual scheduling rules. The system self-adjusts its predictions and recommendations based on patterns it discovers in the data, reducing the need for human intervention and simplifying the overall system architecture while maintaining high operational efficiency
Data Source
AI summary
Aspects of the disclosure provide for managing a fleet of autonomous vehicles of a transportation service. For instance, a plurality of inputs including a current demand for services, predictions about future demand for services, and current status of the fleet may be identified. The current status may include information identifying one of a plurality of predefined states for each autonomous vehicle of the fleet. A schedule may be determined based on the plurality of inputs. The schedule may define a number of autonomous vehicles that should be in each of the plurality of expected future states. The schedule may be used to determine an assignment for each of the autonomous vehicles to one of the plurality of predefined states.