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Eric T. Chancey

Publications and source records attributed to Eric T. Chancey.

10 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2024

Vertiport Management from Simulation to Flight: Continued Human Factors Assessment of Vertiport Operations

The Urban Air Mobility concept envisions a new future for transportation in urban environments, primarily focused around vertiport operations. A vertiport refers to an identifiable ground or elevated area used for the vertical takeoff and landing of an aircraft. One critical role within vertiport operations that has been proposed but is under-researched is the vertiport manager. The vertiport manager is expected to manage the ground-to-air operations at the vertiport, but their precise responsibilities, tasks, and challenges are currently undefined. The High Density Vertiplex Subproject at NASA is responsible for developing and testing technologies, concepts, and architectures that will support the infrastructure needed for terminal environments around vertiports. Our previous work investigated ground control station operators performing simulated and live flight operations within a remote operations environment. In the current study, we extended from our previous work to explore the role of a vertiport manager. Specifically, we investigated five participants serving as a vertiport manager across both simulated and live flight operations. We performed multiple knowledge elicitation techniques, a thematic content analysis of qualitative data, and naturalistic observations to create a list of insights, design and training recommendations, and a simplified cognitive task diagram of the vertiport manager’s primary task. Findings from this study are discussed within the context of the current debate on the degree to which this role should be automated. A third alternative is suggested to allow a human vertiport manager to be successful by teaming with increasingly autonomous systems, designing the role to improve safety and efficiency of Urban Air Mobility operations.

AAM↗
NASA NTRS2023

Ch. 12. A Theoretical Approach to Management of Limited Attentional Resources to Support the m:N Operation in Advanced Air Mobility Ecosystem

Advanced air mobility (AAM) technologies incorporate increasingly autonomous systems that allow fully remote, independent, and intelligent operation of air vehicles to support the transportation of goods and passengers within and across urban and rural areas. With a myriad of automated technologies enabling the AAM ecosystem, the human operator’s role will likely be a passive supervisory monitor of the air vehicles, involving increasingly fewer humans (m) that manage many more autonomous systems (N), or m:N operations. Unfortunately, the human performance literature suggests that human operators will exhibit poor supervision of numerous autonomous agents due to the limits of attentional resources in the operators. In the general human information-processing model, a human operator exercises a limited pool of attentional resources to engage various information-processing stages including detecting, perceiving, comprehending, and predicting objects around them. Yamani and Horrey (2018) expanded the human information-processing model to characterize a tradeoff between information-processing demand and resource relief that automation brings in the context of automated driving. In their model, a driver interacting with an automated driving system is assumed to reallocate resources “freed” by automation to support other information-processing stages required for successful task performance. A future AAM ecosystem enabled by an orchestration of advanced automated systems, however, requires a single operator to interact with more than one air vehicle with varying levels and degrees of automated systems, making the traditional framework of human-automation interaction insufficient. To address this gap, we provide a review of the literature on situation assessment and trust, two constructs identified as critical for a fuller understanding of intimate and intricate interactions between a human operator and multiple air vehicles equipped with increasingly autonomous systems. Then, we propose an expansion of Yamani and Horrey’s (2018) model to motivate systematic research on the human operator’s role, identify factors that influence resource allocation and guide human-centered design of an interface supporting the m:N operation in the AAM environment.

Advanced Air Mobility↗
NASA NTRS2020

Public Trust and Acceptance for Concepts of Remotely Operated Urban Air Mobility Transportation

There is building interest within industry and government to enable Urban Air Mobility (i.e., air-taxies). One concept envisions remotely piloted aircraft, yet it is unclear how this will impact public trust and acceptance. Method: Two hundred participants read vignettes describing remotely-piloted UAM operations and then responded to a series of questionnaires. The study employed a one-way between-subjects design manipulating five levels of Pilot-in-Command Distance: Onboard Pilot; Remote Control Pilot; Dedicated Remote Operator; Remote Operator; System Manager. Results: The Remote Control Pilot group indicated they would be less likely than the Onboard Pilot Group to use UAM, based on the mediating effect of trust in the automation. The Remote Control Pilot and Remote Operator groups indicated they would be less likely to use UAM than the Onboard Pilot group, based on the mediating effect of trust in the remote pilot/operator. Conclusion: Trust in UAM automation and remote pilots/operators will likely affect public acceptance of UAM.

MTurk↗
NASA NTRSDate not supplied

Attentional Considerations in Advanced Air Mobility Operations: Control, Manage, or Assist?

The implementation of automation will enable Advanced Air Mobility (AAM), which could alter the hu-man’s responsibilities from those of an active controller to a passive monitor of vehicles. Mature AAM operations will likely rely on both experienced and novice operators to supervise multiple aircraft. As AAM constitutes a complex and increasingly autonomous system, the human operator’s set of responsibilities will transition from those of a controller, to a manager, and eventually to an assistant to highly automated systems. The development of AAM will require system designers to characterize these three sets of human responsibilities. The present work proposes different human responsibilities across various roles (i.e., pilot in command, system operator, system assistant) in the context of AAM along with pertinent attention-related constructs that could contribute to each of the three identified roles of AAM operators including situation awareness, workload, complacency, and vigilance.

Advanced Air Mobility↗
NASA NTRSDate not supplied

Pathfinder Networks for Measuring Operator Mental Model Structure with a Simple Autopilot System

Pathfinder networks are a method to represent mental models from empirically generated pairwise relatedness ratings. This study examined the effects of training exposure on mental model structures based on relatedness ratings collected using the Target Rating method. Forty-eight participants read instruction slides with or without explicit information on the functionality of an autopilot system (Advanced Mental Model or Basic Mental Model groups, respectively). Participants provided relatedness ratings and completed a comprehension test. The Advanced Mental Model group had more common links with the expected model, higher within-group network similarity scores, and higher mental model assessment questionnaire scores than the Basic Mental Model group. Both groups had coherence scores above the minimum threshold for internal consistency. Pathfinder network analysis was sensitive to changes produced by a simple exposure training intervention. In practice, a simple training program may effectively influence operator mental models in novel technological environments such as Advanced Air Mobility.

Mental Models↗
NASA NTRSDate not supplied

MPATH (Measuring Performance for Autonomy Teaming with Humans) Ground Control Station: Design Approach and Initial Usability Results

Envisioned future Advanced Air Mobility (AAM) operations will require a transition of aircraft command and control from onboard pilots to remote operators. The National Aeronautics and Space Administration (NASA) has developed a research ground control station (GCS) software called MPATH (Measuring Performance for Autonomy Teaming with Humans) to study the human factors of remote operators in a representative AAM environment, where small uncrewed aerial systems (sUAS; simulated or real) act as surrogates for larger AAM aircraft. A primary focus of the research being conducted with the MPATH GCS is scalability (i.e., one human managing multiple vehicles). MPATH has demonstrated to be a useful capability for human factors research and remote operations. Two initial usability studies and a multi-vehicle control assessment were recently conducted, and usability data and operator feedback were collected. Generally, participants rated MPATH high on usability and interface quality, whereas information quality was rated slightly lower. These results were supported by specific feedback. Several generalized GCS design recommendations are proposed based on the results and feedback from these flight activities. Future updates to MPATH will incorporate the proposed recommendations. In practice, these recommendations could be used by any GCS software designer or developer to promote usability and safety.

Advanced Air Mobility↗
NASA NTRSDate not supplied

The Influence of Viability, Independence, and Self-Governance on Trust and Public Acceptance of Uncrewed Air Vehicle Operations

Trust is expected to be a critical construct that drives successful use of advanced air mobility (AAM) technologies. As yet, though, the role of trust in human-autonomy interaction is underexplored. Kaber (2018) argues that autonomy requires the highest level of three independent dimensions -- viability, independence, and self-governance. The present study examined whether trust varies across the three dimensions of autonomy under varying levels of risk. Participants in the high-risk group read a series of vignettes on a drone that delivers medical supplies over a city where the current study was conducted. Participants in the low-risk group read a series of vignettes on a drone that delivers fast food over a fictitious city. Each vignette described a drone that is either autonomous (i.e., possesses all dimensions) or automated (i.e., one of the dimensions is compromised). Results imply that the three dimensions of autonomy do not equally influence human-technology trust and behavior.

Advanced Air Mobility↗