Human-AI Task Sequences for Remote Autonomous Vehicle Support
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Solution Overview
Problem
Current remote assistance systems for autonomous vehicles lack efficiency in response time and task distribution due to the absence of a routine for handling similar conditions, leading to operator uncertainty about AI capabilities and needs.
Innovation Solution
Implementing a human-AI decision-making system using predefined dynamic task sequences that delegate tasks to multiple agents, including human and AI, to resolve conditions through a human-autonomy teaming manager, which initiates dialogues, receives human input, and manages task sequences for efficient condition resolution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If remote assistance systems handle each condition individually without predefined routines, then system flexibility is maintained, but response time increases and operator uncertainty about AI capabilities arises
Solution Approach 1:
The patent implements predefined dynamic task sequences that are prepared in advance for various vehicle conditions. These sequences include predetermined workflows for common scenarios such as obstacle detection, traffic violation handling, and mechanical issues. When a condition is detected, the system automatically retrieves and executes the corresponding predefined sequence, eliminating the need for operators to develop new protocols each time and significantly reducing response time.
Solution Approach 2:
The task sequences are designed to be dynamic rather than static, allowing them to adapt based on real-time conditions. The system can modify the execution flow, add or remove tasks, and adjust priorities based on the current operational context, vehicle state, and environmental factors. This dynamic nature maintains system flexibility while providing structured guidance to reduce response time.
2Reliability
If tasks are distributed to multiple agents including human operators, then decision-making quality improves, but coordination complexity increases
Solution Approach 1:
The patent segments the remote assistance system into distinct functional agents with specific responsibilities: an autonomous mobility agent for vehicle monitoring, a human-autonomy teaming manager for coordination, and human operators for decision-making. Each agent handles specific task types, and the segmentation is managed through a standardized interface that defines clear communication protocols, reducing coordination complexity while maintaining collaborative decision-making quality.
Solution Approach 2:
The human-autonomy teaming manager serves as an intermediary layer between the autonomous mobility agent and human operators. It receives task requests, determines appropriate task sequences, coordinates resource allocation, and manages communication between agents. This intermediary structure simplifies coordination by providing a centralized control point that handles the complexity of multi-agent interaction, allowing agents to focus on their specialized functions.
3Productivity
If predefined dynamic task sequences are implemented, then task delegation efficiency improves, but system adaptability to novel conditions may decrease
Solution Approach 1:
The task sequences are designed to be dynamic and adaptable rather than rigid. The system can modify existing sequences based on novel conditions by adding new tasks, adjusting task parameters, or reordering steps. The dynamic nature allows the system to maintain efficiency through predefined structures while adapting to new scenarios by updating the sequence definitions, thus resolving the contradiction between efficiency and adaptability.
Solution Approach 2:
The system incorporates feedback mechanisms where outcomes of executed task sequences are analyzed and used to refine future sequences. When novel conditions are encountered, the system learns from the resolution process and updates its predefined sequences accordingly. This feedback loop enables the system to maintain high task delegation efficiency through standardized procedures while improving adaptability to novel conditions through continuous learning and sequence optimization.
Data Source
AI summary
An apparatus for traversing a vehicle transportation network by an autonomous vehicle (AV) includes a human-autonomy teaming manager. The manager includes a first processor configured to initiate a dialogue associated with a predefined dynamic task sequence that is responsive to a condition experienced by the AV while traversing from a starting location to an ending location within the vehicle transportation network. The sequence is one of a plurality of predefined dynamic task sequences that uses multiple agents to resolve the condition. The manager receives, from an interface accessible to a human agent, an input responsive to the dialogue. The input confirms the sequence as a selected predefined dynamic task sequence or selects an other of the plurality of predefined dynamic task sequences as the selected predefined dynamic task sequence. The manager delegates tasks of the selected predefined dynamic task sequence to resolve the condition.