Vehicle Control Planning Device for Operator Workload Reduction
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Solution Overview
Problem
Operators controlling unmanned vehicles face high stress levels and increased error probability due to the need for rapid decision-making in complex missions, leading to prolonged mission times and increased risk, as fully automated systems lack context awareness and often require human confirmation to ensure safety and effectiveness.
Innovation Solution
A computer-implemented method and planning device that generates a mission plan by predicting operator attention demands, minimizing temporal overlap of tasks, and providing optimized task scheduling to reduce operator workload and demand peaks, thereby supporting operators in controlling vehicles with reduced stress and improved efficiency and safety.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If fully automated systems are used for vehicle control, then operational efficiency and response time are improved, but context awareness and safety monitoring deteriorate
Solution Approach 1:
The system segments control functions by dividing tasks into autonomous vehicle operations and operator oversight responsibilities. The planning device handles automated mission planning and vehicle control, while the operator focuses on high-level decision-making and safety monitoring, creating a layered control architecture that optimizes both efficiency and safety
Solution Approach 2:
The planning device acts as an intermediary between the operator and the vehicle systems. It translates operator intent into detailed mission plans, automates routine decisions, and presents refined options to the operator, reducing cognitive load while maintaining safety through structured human-machine collaboration
2Reliability
If operator confirmation is required for system decisions, then safety is improved, but mission time and operational efficiency deteriorate
Solution Approach 1:
The system applies partial automation by requiring operator confirmation only for critical decisions and high-level mission parameters, while allowing autonomous execution for routine operations. This selective approach maintains safety for important decisions without creating bottlenecks for all operations
Solution Approach 2:
The planning device performs preliminary analysis and prepares multiple mission plan options in advance, pre-processing information so that operators only need to review and select from pre-evaluated alternatives rather than making decisions from scratch, reducing confirmation time while maintaining safety
3Reliability
If multiple operator tasks are executed consecutively, then task completion is ensured, but waiting times and mission duration increase
Solution Approach 1:
The system dynamically adjusts the execution sequence and timing of operator tasks based on mission priorities, vehicle states, and operator availability. The planning device reorders tasks in real-time to minimize waiting times while ensuring critical tasks are completed, creating a flexible rather than rigid task schedule
Solution Approach 2:
The system replaces manual task scheduling with an automated planning device that uses algorithms to optimize task sequencing. This computational approach substitutes the mechanical constraint of fixed consecutive execution with dynamic optimization, reducing idle time while maintaining task completion reliability
4Adaptability or versatility
If manual planning of mission routes is performed, then mission customization is improved, but planning complexity and time consumption increase
Solution Approach 1:
The planning device performs preliminary mission analysis and automatically generates initial route plans based on mission parameters, vehicle capabilities, and environmental constraints. This pre-processing provides operators with customized baseline plans that can be further refined, reducing the complexity of manual planning while maintaining adaptability
Solution Approach 2:
The system uses parameter-based planning where operators define high-level mission parameters (objectives, constraints, priorities) and the planning device automatically translates these into detailed customized mission plans. This parameter-driven approach maintains mission customization while reducing the complexity of manual step-by-step planning
Data Source
Figure 1~2b
Figure 3
Figure 4a~4b
AI summary
The present disclosure relates to vehicle control support. In particular, the present disclosure relates to a computer-implemented method comprising for vehicle control support. A planning device obtains mission information via an input unit of the planning device, the mission information at least comprising mission task information for at least one mission task. A processing unit of the planning device then generates operator task information for at least one operator task on the basis of the mission task information and a task model that is stored in the processing unit, wherein the operator task information comprises operator demand information. A mission plan is then generated on the basis of the mission task information, the operator task information, and the operator demand information using a cost metric stored in the processing unit.