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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Reachability Analysis for FollowerStopper: Safety Analysis and Experimental Results

Motivated by earlier work and the developer of a new algorithm, the FollowerStopper, this article uses reachability analysis to verify the safety of the FollowerStopper algorithm, which is a controller designed for dampening stop-and-go traffic waves. With more than 1100 miles of driving data collected by our physical platform, we validate our analysis results by comparing it to human driving behaviors. The FollowerStopper controller has been demonstrated to dampen stop-and-go traffic waves at low speed, but previous analysis on its relative safety has been limited to upper and lower bounds of acceleration. To expand upon previous analysis, reachability analysis is used to investigate the safety at the speeds it was originally tested and also at higher speeds. Two formulations of safety analysis with different criteria are shown: distance-based and time headway-based. The FollowerStopper is considered safe with distance-based criterion. However, simulation results demonstrate that the FollowerStopper is not representative of human drivers - it follows too closely behind vehicles, specifically at a distance human would deem as unsafe. On the other hand, under the time headway-based safety analysis, the FollowerStopper is not considered safe anymore. A modified FollowerStopper is proposed to satisfy time-based safety criterion. Simulation results of the proposed FollowerStopper shows that its response represents human driver behavior better.

Chou, Fang-Chieh↗
DOE OSTI2021

COVID-19 dynamics across the US: A deep learning study of human mobility and social behavior

This paper presents a deep learning framework for epidemiology system identification from noisy and sparse observations with quantified uncertainty. The proposed approach employs an ensemble of deep neural networks to infer the time-dependent reproduction number of an infectious disease by formulating a tensor-based multi-step loss function that allows us to efficiently calibrate the model on multiple observed trajectories. The method is applied to a mobility and social behavior-based SEIR model of COVID-19 spread. The model is trained on Google and Unacast mobility data spanning a period of 66 days, and is able to yield accurate future forecasts of COVID-19 spread in 203 US counties within a time-window of 15 days. Interestingly, a sensitivity analysis that assesses the importance of different mobility and social behavior parameters reveals that attendance of close places, including workplaces, residential, and retail and recreational locations, has the largest impact on the effective reproduction number. Furthermore, the model enables us to rapidly probe and quantify the effects of government interventions, such as lock-down and re-opening strategies. Taken together, the proposed framework provides a robust workflow for data-driven epidemiology model discovery under uncertainty and produces probabilistic forecasts for the evolution of a pandemic that can judiciously provide information for policy and decision making. All codes and data accompanying this manuscript are available at https://github.com/PredictiveIntelligenceLab/DeepCOVID19.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Discerning Deception: An Empirically-Driven Agent-Based Model of Expert Evaluation of Scientific Content

Both human subject experiments and computational, modeling and simulations have been used to study detection of deception. This work aims to combine these two methods by integrating empirically-derived information (from human subject experiments) into agent-based models to generate novel insights into the complex problems of detection of disinformation content. Computational experiments are used to simulate across multiple scenarios for evaluation and decision-making regarding the validity of potentially deceptive scientific documents. Factors influencing the human agent behaviors in the model were identified through a human subject experiment that was conducted to evaluate and characterize decision making related to disinformation discernment. Correlation and regression analyses were used to translate insights from the human subjects experiment to inform the parameterization of agent features and scenario development. Three scenarios were evaluated with the agent-based models to help evaluate the replicability of the simulations (validation analysis) and assess the influence of human agent and document features (sensitivity analyses). A replication of the human participant experiment demonstrated that the agent-based simulations compare favorably to empirical findings. The agent-based modeling was then used to conduct sensitivity analysis on the accuracy of deception detection as a function of document proportions and human agent features. Results indicate that precision values are adversely impacted when the proportion of deceptive documents is lower in the overall sample, whereas recall values are more sensitive to changes in human agent features. These findings indicate important nuances in accuracy evaluations that should be further considered (including consideration of potential alternate metrics) in future agent-based models of disinformation. Additional areas for future exploration include extension of simulations to consider other ways to align the agent-based model design with psychological theory and inclusion of agent-agent interactions, especially as it pertains to sharing of scientific information within an organizational context.

99 GENERAL AND MISCELLANEOUS↗
NASA NTRS1995

CaseMIDAS - A reactive planning architecture for the man-machine integration design and analysis system

MIDAS is a set of tools which allow a designer to specify the physical and functional characteristics of a complex system such as an aircraft cockpit, and analyze the system with regard to human performance. MIDAS allows for a number of static analyses such as military standard reach and fit analysis, display legibility analysis, and vision polars. It also supports dynamic simulation of mission segments with 3d visualization. MIDAS development has incorporated several models of human planning behavior. The CaseMIDAS effort has been to provide a simplified and unified approach to modeling task selection behavior. Except for highly practiced, routine procedures, a human operator exhibits a cognitive effort while determining what step to take next in the accomplishment of mission tasks. Current versions of MIDAS do not model this effort in a consistent and inclusive manner. CaseMIDAS also attempts to address this issue. The CaseMIDAS project has yielded an easy to use software module for case creation and execution which is integrated with existing MIDAS simulation components.

Pease, R. Adam↗
NASA NTRS2004

Mobile Autonomous Humanoid Assistant

A mobile autonomous humanoid robot is assisting human co-workers at the Johnson Space Center with tool handling tasks. This robot combines the upper body of the National Aeronautics and Space Administration (NASA)/Defense Advanced Research Projects Agency (DARPA) Robonaut system with a Segway(TradeMark) Robotic Mobility Platform yielding a dexterous, maneuverable humanoid perfect for aiding human co-workers in a range of environments. This system uses stereo vision to locate human team mates and tools and a navigation system that uses laser range and vision data to follow humans while avoiding obstacles. Tactile sensors provide information to grasping algorithms for efficient tool exchanges. The autonomous architecture utilizes these pre-programmed skills to form human assistant behaviors. The initial behavior demonstrates a robust capability to assist a human by acquiring a tool from a remotely located individual and then following the human in a cluttered environment with the tool for future use.

Diftler, M. A.↗
NASA NTRS2014

A Closer Look at Automation Behavior During a Human-in-the-Loop Simulation

A 2012 Human-In-The-Loop air traffic control simulation investigated a gradual paradigm-shift in the allocation of functions between operators and automation. Air traffic controllers staffed five adjacent high-altitude en route sectors, and during the course of a two-week experiment, worked traffic under four different function allocation concepts aligned with increasingly mature NextGen operational environments. These NextGen time-frames ranged from near current-day operations to nearly fully-automated control, in which the ground systems automation was responsible for detecting conflicts, issuing strategic and tactical resolutions, and alerting controllers to exceptional circumstances. This paper continues the investigations reported in previous publications. Analyses of data surrounding the conflict-resolution task serve as the context in which we investigate the interactions between controllers and the automation.

Human-Automation Interaction↗
NASA NTRS2017

Autonomous, Context-Sensitive, Task Management Systems and Decision Support Tools I: Human-Autonomy Teaming Fundamentals and State of the Art

Recent advances in artificial intelligence, machine learning, data mining and extraction, and especially in sensor technology have resulted in the availability of a vast amount of digital data and information and the development of advanced automated reasoners. This creates the opportunity for the development of a robust dynamic task manager and decision support tool that is context sensitive and integrates information from a wide array of on-board and off aircraft sourcesa tool that monitors systems and the overall flight situation, anticipates information needs, prioritizes tasks appropriately, keeps pilots well informed, and is nimble and able to adapt to changing circumstances. This is the first of two companion reports exploring issues associated with autonomous, context-sensitive, task management and decision support tools. In the first report, we explore fundamental issues associated with the development of an integrated, dynamic, flight information and automation management system. We discuss human factors issues pertaining to information automation and review the current state of the art of pilot information management and decision support tools. We also explore how effective human-human team behavior and expectations could be extended to teams involving humans and automation or autonomous systems.

context-sensitive↗
NASA NTRSDate not supplied

Behavioral Health and Performance Operations and Research in Human Spaceflight

There are two distinct functions of the Behavioral Health and Performance (BHP) specialists at NASA Johnson Space Center (JSC), with a shared goal of promoting optimal performance during all mission phases, beginning with selection to the NASA Astronaut Corps through training for and completing a spaceflight mission. The BHP Operations team is active in areas such as selection, training, routine behavioral healthcare services, and work-rest schedule. The BHP Laboratory is engaged in research in space and analog platforms to address the risks and other challenges associated with future deep space missions. Their research focuses on topics that align with the NASA Human Research Program’s Human Factors Behavioral Performance Element goals. This panel will provide details of recent work by BHP Operations and the BHP Laboratory at NASA JSC, and will demonstrate their shared goals, and contributions toward, optimizing performance and maintaining the behavioral health and well-being of astronauts during all mission phases.In 2020-2021, NASA engaged another astronaut applicant screening process to identify its 23rd group of astronaut candidates. The BHP Operations team completed a significant amount of work to evaluate applicants and provide valuable input to the Astronaut Selection Board and the Aerospace Medical Board. One presentation will provide an overview of the work completed by BHP; and one presentation will discuss a critical competency important for successful job performance of the astronauts – Team Orientation. The former will summarize aspects of BHP’s Select-in and Select-out process and provide lessons learned for future selection cycles; the latter will briefly discuss the competencies of BHP’s applicant screening work, with focus on the importance of Team Orientation and the challenges of its measure. A third presentation will provide an overview of BHP’s current fatigue management work with astronauts and mission-critical operators at JSC (e.g., flight directors, pilots), and provide a look at the direction these fatigue management services may take in future exploration missions. Two other presentations will focus on the research completed by the BHP Lab to measure in a standardized way the critical aspects of behavioral health and well-being. One of the presentations will provide an overview of the Exploration Measures being collected during all space and analog missions, and the importance these objective measures have for future research and operations. The final presentation will summarize analyses completed with these measures, as they have been harmonized across both ISS and Analog missions.

Gary Beven↗
NASA NTRS1981

Studies of planning behavior of aircraft pilots in normal, abnormal, and emergency situations

A methodology for the study of human planning behavior in complex dynamic systems is presented and applied to the study of aircraft pilot behavior in normal, abnormal and emergency situations. The method measures the depth of planning, that is the level of detail employed with respect to a specific task, according to responses to a verbal questionnaire, and compares planning depth with variables relating to time, task criticality and the probability of increased task difficulty. In two series of experiments, depth of planning was measured on a five- or ten-point scale during various phases of flight in a HFB-320 simulator under normal flight conditions, abnormal scenarios involving temporary runway closure due to snow removal or temporary CAT-III conditions due to a dense fog, and emergency scenarios involving engine shut-down or hydraulic pressure loss. Results reveal a dichotomy between event-driven and time-driven planning, different effects of automation in abnormal and emergency scenarios and a low correlation between depth of planning and workload or flight performance.

Johannsen, G.↗
NASA NTRS2017

Effects of Eye Measures on Human Controller Remnant and Control Behavior

The aim of the current research was to investigate the possible relation between changes in eye activity parameters, variations in human remnant at the perceptual level and changes in human operator model parameters. Fourteen subjects performed a pitch tracking task, in which the display brightness was varied by changing the background color around a simplified primary flight display, in order to create a controlled, quasilinear change in the pupil diameter through the pupillary light reflex. Pupil diameter, blink, eye opening, and opening and closing amplitudes and speeds were recorded using an eye tracker. Participants controlled single integrator-like and double integrator-like dynamics. The variation in pupil diameter did not introduce significant differences in neither remnant characteristics nor the human operator model parameters. An interesting effect occurred in the human controller’s time delay for the single integrator task, where the time delay was significantly higher for the darkest brightness compared to the other levels of brightness. This effect was not observed for the double integrator dynamics. Data suggested that the more difficult controlled dynamics induced a squinting effect, visible in smaller eye opening, and smaller eye opening and closing amplitudes. These results suggest that performance, and control behavior are invariant to the display brightness. Moreover, monitoring changes in the eye activity could represent a method of predicting variations in human remnant characteristics and human controller model parameters, introduced by task difficulty.

Eye tracking↗
NASA NTRS2005

Human Performance Models of Pilot Behavior

Five modeling teams from industry and academia were chosen by the NASA Aviation Safety and Security Program to develop human performance models (HPM) of pilots performing taxi operations and runway instrument approaches with and without advanced displays. One representative from each team will serve as a panelist to discuss their team s model architecture, augmentations and advancements to HPMs, and aviation-safety related lessons learned. Panelists will discuss how modeling results are influenced by a model s architecture and structure, the role of the external environment, specific modeling advances and future directions and challenges for human performance modeling in aviation.

Foyle, David C.↗
NASA NTRS1981

A theory of human error

Human errors tend to be treated in terms of clinical and anecdotal descriptions, from which remedial measures are difficult to derive. Correction of the sources of human error requires an attempt to reconstruct underlying and contributing causes of error from the circumstantial causes cited in official investigative reports. A comprehensive analytical theory of the cause-effect relationships governing propagation of human error is indispensable to a reconstruction of the underlying and contributing causes. A validated analytical theory of the input-output behavior of human operators involving manual control, communication, supervisory, and monitoring tasks which are relevant to aviation, maritime, automotive, and process control operations is highlighted. This theory of behavior, both appropriate and inappropriate, provides an insightful basis for investigating, classifying, and quantifying the needed cause-effect relationships governing propagation of human error.

Mcruer, D. T.↗
NASA NTRS2011

Estimation of Time-Varying Pilot Model Parameters

Human control behavior is rarely completely stationary over time due to fatigue or loss of attention. In addition, there are many control tasks for which human operators need to adapt their control strategy to vehicle dynamics that vary in time. In previous studies on the identification of time-varying pilot control behavior wavelets were used to estimate the time-varying frequency response functions. However, the estimation of time-varying pilot model parameters was not considered. Estimating these parameters can be a valuable tool for the quantification of different aspects of human time-varying manual control. This paper presents two methods for the estimation of time-varying pilot model parameters, a two-step method using wavelets and a windowed maximum likelihood estimation method. The methods are evaluated using simulations of a closed-loop control task with time-varying pilot equalization and vehicle dynamics. Simulations are performed with and without remnant. Both methods give accurate results when no pilot remnant is present. The wavelet transform is very sensitive to measurement noise, resulting in inaccurate parameter estimates when considerable pilot remnant is present. Maximum likelihood estimation is less sensitive to pilot remnant, but cannot detect fast changes in pilot control behavior.

Zaal, Peter M. T.↗