Autonomous Fleet Scheduling to Avoid Teleoperator Overlap
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Solution Overview
Problem
Managing a fleet of autonomous or remotely operated vehicles requires efficient and safe control, as existing solutions often necessitate multiple human observers or teleoperators, leading to inefficiencies and safety concerns due to potential overlaps in demand for assistance or remote driving.
Innovation Solution
Assigning pre-determined routes with specific road segments that require teleoperator assistance and determining time overlaps to insert delays or speedups in autonomous driving segments, allowing vehicles to avoid simultaneous need for teleoperator intervention, thus optimizing the use of human operators and ensuring safety.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple human observers or teleoperators are assigned to monitor and remotely control autonomous vehicles, then safety and reliability improve, but system complexity and operational costs increase
Solution Approach 1:
The system performs preliminary analysis of route characteristics and teleoperator demand patterns before deployment. By pre-identifying road segments that require teleoperator assistance and predicting time overlaps in advance, the system optimizes teleoperator allocation beforehand, reducing the need for excessive human operators while maintaining safety
Solution Approach 2:
The system continuously monitors actual teleoperator demand versus predicted demand, and uses this feedback to refine future predictions and adjustments. This closed-loop feedback mechanism enables the system to learn from operational data and improve its efficiency over time, reducing the number of teleoperators needed while maintaining reliable safety coverage
2Reliability
If more teleoperators are deployed to handle peak demand, then reliability improves, but operational efficiency and cost-effectiveness deteriorate
Solution Approach 1:
The system calculates predicted teleoperator demand and identifies time overlaps before vehicles are deployed on their routes. By inserting delays or speedups in advance during autonomous driving segments, the system proactively prevents peak demand occurrences, eliminating the need to deploy additional teleoperators for peak coverage while maintaining operational efficiency
Solution Approach 2:
The system dynamically adjusts vehicle speed parameters during autonomous driving segments to optimize teleoperator utilization. By changing speed parameters to create temporal separation in teleoperator demand patterns, the system reduces peak demand without compromising delivery time intervals, thereby improving operational efficiency while maintaining reliability
3Productivity
If vehicles are allowed to operate with fewer teleoperators, then operational efficiency improves, but the risk of vehicles being stranded on roads increases
Solution Approach 1:
The system performs preliminary routing optimization to ensure that even with reduced teleoperator coverage, every vehicle will reach its destination or a safe location before teleoperator assistance is needed. By pre-planning routes and adjusting speeds to match available teleoperator capacity, the system guarantees vehicle availability without requiring excessive human operators
Solution Approach 2:
The system introduces an intelligent routing and scheduling intermediary layer that coordinates between autonomous vehicles and available teleoperators. This intermediary optimizes the matching of vehicles to teleoperators based on predicted demand, ensuring that fewer teleoperators can effectively support more vehicles while maintaining reliability through optimized resource allocation
4Adaptability or versatility
If teleoperator assistance is provided on-demand without advance scheduling, then flexibility improves, but peaks in demand occur causing inefficiency
Solution Approach 1:
The system performs preliminary analysis of route characteristics and predicts teleoperator demand for each road segment before vehicles begin their journeys. By identifying time overlaps in advance and adjusting vehicle schedules proactively, the system smooths out demand peaks while maintaining the flexibility to adapt to actual conditions, thereby improving teleoperator utilization efficiency without sacrificing adaptability
Data Source
AI summary
Embodiments of the invention relate to a method for controlling a fleet of at least two autonomous/remotely operated vehicles, wherein an embodiment of the method comprises: assigning to each of said at least two vehicles a predetermined route comprising a road segment which requires that a teleoperator of a remote operation station assists and/or drives the vehicle, determining a time overlap of the said road segments, and based on the determined time overlap, inserting a delay or a speedup in a preceding autonomous drive road segment of the predetermined route of at least one of said at least two autonomous/remotely operated vehicles so that said road segments no longer overlap in time.