Teammate State Estimation for AV Coordination Under Communication Denial
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Solution Overview
Problem
In autonomous vehicle (AV) mission systems, communication denial due to radio jamming, environmental hazards, or adversarial threats disrupts the ability of AVs to share mission data, leading to inaccurate teammate navigation state predictions and ineffective decision-making, especially in contested battle spaces and urban environments.
Innovation Solution
A mission system that uses a simulated teammate guidance and avoidance module to generate high-fidelity estimated navigation states for teammate AVs during communication denial, ensuring continuous real-time prediction of motion and decision-making by simulating behavior, guidance, and avoidance control hierarchies, reducing uncertainty and maintaining mission continuity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If communication signals are transmitted between AVs using radio frequency, then real-time mission data sharing is achieved, but communication denial occurs due to radio jamming or environmental hazards
Solution Approach 1:
Each AV pre-loads and stores complete mission data sets (including navigation trajectories, objective locations, and operational parameters) before communication denial occurs. This preliminary action ensures that when radio communication is jammed or denied, each vehicle already possesses the necessary information to continue mission operations independently without losing critical mission data
Solution Approach 2:
The system creates and maintains identical copies of mission data across all AVs in the fleet. Each vehicle stores a complete replica of the mission dataset, ensuring that if one AV loses communication, the mission information is preserved and accessible through other vehicles or local storage, preventing information loss due to communication denial
2Device complexity
If naive forecasting methods are used to predict teammate navigation states during communication denial, then computational simplicity is maintained, but prediction accuracy deteriorates significantly
Solution Approach 1:
Each AV executes a complete simulation of the teammate's guidance and avoidance control hierarchy using its own onboard computational resources. This self-service approach allows each vehicle to independently generate accurate predictions of teammate navigation states without relying on external computational systems or simplified models, achieving high prediction accuracy while maintaining system independence during communication denial
3Measurement precision
If simulated teammate guidance and avoidance module is executed on each AV, then high-fidelity navigation state estimation is achieved, but computational load and processing time increase
Solution Approach 1:
The guidance and avoidance control hierarchy is pre-programmed and stored in memory on each AV before mission execution. During communication denial, each vehicle activates this pre-loaded simulation module, which runs using stored algorithms and local sensor data. This preliminary preparation reduces real-time computational energy consumption while maintaining high-fidelity navigation state estimation accuracy
Solution Approach 2:
The simulation executes only the specific guidance and avoidance functions necessary for navigation state prediction, rather than running complete vehicle control simulations. This partial action approach focuses computational resources on the critical estimation functions, reducing overall energy consumption while maintaining sufficient accuracy for mission coordination
4Duration of action of stationary object
If AVs operate independently during communication denial, then mission continuity is maintained, but coordinated decision-making capability is reduced
Solution Approach 1:
Each AV continuously monitors its own navigation state and the estimated states of teammates through the simulation module. This feedback mechanism allows vehicles to detect changes in the operational environment and adjust their behavior accordingly. When communication is restored, the accumulated feedback information enables rapid re-synchronization and coordinated decision-making, maintaining both mission continuity during denial and adaptability when communication resumes
Data Source
AI summary
A mission system for autonomous vehicle (AV) team coordination and a method of using the same are disclosed. A controller included on an AV shares mission data between two or more AVs, and in response to communication denial, generates estimated navigation trajectories for teammate AVs. A simulation outputs estimated navigation states for the teammate AVs. The estimated navigation states are identical or substantially identical to navigation states otherwise generated by controllers included on the teammate AVs. The estimated navigation trajectories are generated based on the estimated navigation states.