scholarly journals The Adaptive Automation Design

10.5772/5878 ◽  
2008 ◽  
Author(s):  
Caterina Calefato ◽  
Roberto Montanari ◽  
Francesco Tesauri
Keyword(s):  
Adaptive Automation

A brain-based adaptive automation system and situation awareness: The role of complacency potential

2003 ◽  
Author(s):  
Nathan R. Bailey ◽  
Mark W. Scerbo ◽  
Frederick G. Freeman ◽  
Peter J. Mikulka ◽  
Lorissa A. Scott
Keyword(s):  
Situation Awareness ◽  
Automation System ◽  
Adaptive Automation

Adaptive Automation and Human Performance: 1. Multi-Task Performance Characteristics

1992 ◽  
Author(s):  
Raja Parasuraman ◽  
Toufik Bahri ◽  
Robert Molloy
Keyword(s):  
Task Performance ◽  
Human Performance ◽  
Performance Characteristics ◽  
Adaptive Automation

scholarly journals Adaptive Human-Robot Interactions for Multiple Unmanned Aerial Vehicles

Robotics ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 12
Author(s):  
Yixiang Lim ◽  
Nichakorn Pongsarkornsathien ◽  
Alessandro Gardi ◽  
Roberto Sabatini ◽  
Trevor Kistan ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Real Time ◽  
Simulation Environment ◽  
Adaptive Automation ◽  
Cognitive States ◽  
Aerial Vehicles ◽  
Machine Interface ◽  
Multiple Unmanned Aerial Vehicles ◽  
Online Adaptation ◽  
Adaptation Phase

Advances in unmanned aircraft systems (UAS) have paved the way for progressively higher levels of intelligence and autonomy, supporting new modes of operation, such as the one-to-many (OTM) concept, where a single human operator is responsible for monitoring and coordinating the tasks of multiple unmanned aerial vehicles (UAVs). This paper presents the development and evaluation of cognitive human-machine interfaces and interactions (CHMI2) supporting adaptive automation in OTM applications. A CHMI2 system comprises a network of neurophysiological sensors and machine-learning based models for inferring user cognitive states, as well as the adaptation engine containing a set of transition logics for control/display functions and discrete autonomy levels. Models of the user’s cognitive states are trained on past performance and neurophysiological data during an offline calibration phase, and subsequently used in the online adaptation phase for real-time inference of these cognitive states. To investigate adaptive automation in OTM applications, a scenario involving bushfire detection was developed where a single human operator is responsible for tasking multiple UAV platforms to search for and localize bushfires over a wide area. We present the architecture and design of the UAS simulation environment that was developed, together with various human-machine interface (HMI) formats and functions, to evaluate the CHMI2 system’s feasibility through human-in-the-loop (HITL) experiments. The CHMI2 module was subsequently integrated into the simulation environment, providing the sensing, inference, and adaptation capabilities needed to realise adaptive automation. HITL experiments were performed to verify the CHMI2 module’s functionalities in the offline calibration and online adaptation phases. In particular, results from the online adaptation phase showed that the system was able to support real-time inference and human-machine interface and interaction (HMI2) adaptation. However, the accuracy of the inferred workload was variable across the different participants (with a root mean squared error (RMSE) ranging from 0.2 to 0.6), partly due to the reduced number of neurophysiological features available as real-time inputs and also due to limited training stages in the offline calibration phase. To improve the performance of the system, future work will investigate the use of alternative machine learning techniques, additional neurophysiological input features, and a more extensive training stage.

Effects of a Psychophysiological System for Adaptive Automation on Performance, Workload, and the Event-Related Potential P300 Component

2003 ◽  
Vol 45 (4) ◽  
pp. 601-614 ◽  
Author(s):  
Lawrence J. Prinzel ◽  
Frederick G. Freeman ◽  
Mark W. Scerbo ◽  
Peter J. Mikulka ◽  
Alan T. Pope
Keyword(s):  
Event Related Potential ◽  
Adaptive Automation ◽  
P300 Component

scholarly journals Adaptive Automation Triggered by EEG-Based Mental Workload Index: A Passive Brain-Computer Interface Application in Realistic Air Traffic Control Environment

2016 ◽  
Vol 10 ◽  
Author(s):  
Pietro Aricò ◽  
Gianluca Borghini ◽  
Gianluca Di Flumeri ◽  
Alfredo Colosimo ◽  
Stefano Bonelli ◽  
...  
Keyword(s):  
Traffic Control ◽  
Air Traffic Control ◽  
Mental Workload ◽  
Brain Computer Interface ◽  
Air Traffic ◽  
Computer Interface ◽  
Control Environment ◽  
Adaptive Automation

Allocation of Function, Adaptive Automation, and Fault Management

1998 ◽  
Vol 42 (3) ◽  
pp. 277-279
Author(s):  
Neville Moray ◽  
Toshiuki Inagaki ◽  
Makoto Itoh
Keyword(s):  
Process Control ◽  
Continuous Process ◽  
Fault Management ◽  
Control Task ◽  
Adaptive Automation ◽  
Human Control ◽  
Self Confidence ◽  
Levels Of Automation ◽  
Allocation Of Function ◽  
Different Levels

Sheridan's “Levels of Automation” were explored in an experiment on fault management of a continuous process control task which included situation adaptive automation. Levels of automation with more or less automation autonomy, and different levels of advice to the operator were compared, with automatic diagnosis whose reliability varied. The efficiency of process control and of fault management were explored under human control and automation in fault management, and aspects of the task in which human or automation were the more efficient defined. The results are related to earlier work on trust and self confidence in allocation of function by Lee, Moray, and Muir.

Adaptive Automation: From Theory to Practice

2001 ◽  
Vol 45 (4) ◽  
pp. 291-292 ◽  
Author(s):  
Raja Parasuraman
Keyword(s):  
Adaptive Automation ◽  
Theory To Practice
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