Engineering topics

Lachter, Joel

Publications and source records attributed to Lachter, Joel.

At least 19 records

NASA NTRS2018

Crew Resource Management for Automated Teammates (CRM-A)

Crew Resource Management (CRM) is the application of human factors knowledge and skills to ensure that teams make effective use of all resources. This includes ensuring that pilots bring in opinions of other teammates and utilize their unique capabilities. CRM was originally developed 40 years ago in response to a number of airline accidents in which the crew was found to be at fault. The goal was to improve teamwork among airline cockpit crews. The notion of "team" was later expanded to include cabin crew and ground resources. CRM has also been adopted by other industries, most notably medicine. Automation research now finds itself faced with similar issues to those faced by aviation 40 years ago: how to create a more robust system by making full use of both the automation and its human operators. With advances in machine intelligence, processing speed and cheap and plentiful memory, automation has advanced to the point that it can and should be treated as a teammate to fully take advantage of its capabilities and contributions to the system. This area of research is known as Human-Autonomy Teaming (HAT). Research on HAT has identified reusable patterns that can be applied in a wide range of applications. These patterns include features such as bi-directional communication and working agreements. This paper will explore the synergies between CRM and HAT. We believe that HAT research has much to learn from CRM and that there are benefits to expanding CRM to cover automation.

Crew Resource Management↗

NASA NTRS2018

Human Automation Teaming: Lessons Learned and Future Directions

Full autonomy seems to be the goal for system developers in almost every area of the economy. However, as we move from automated systems to autonomous systems, designers have needed to insert humans to oversee automation that has traditionally been brittle or incomplete. This creates its own problems as the operator is usually out of the loop when the automation hands over problems that it cannot handle. To better handle these situations, it has been proposed that we develop human automation teams that have shared goals and objectives to support task performance. This paper will describe an initial model of Human Automation Teaming (HAT) which has three elements: transparency, bi-directional communications, and human-directed execution. Transparency in our model is a method for giving insight into the reasoning behind automated recommendations and actions, bi-directional communication allows the operator to communicate directly with the automation, and finally the automation defers execution to the human. The model was implemented through a number of features on an electronic flight bag (EFB) which are described in the paper. The EFB was installed in a mid-fidelity flight simulator and used by 12 airline pilots to support diversion decisions during off-nominal flight scenarios. Pilots reported that working with the HAT automation made diversion decisions easier and reduced their workload. They also reported that the information provided about diversion airports was similar to what they would receive from ground dispatch, thus making coordination with dispatch easier and less time consuming. These HAT features engender more trust in the automation when appropriate, and less when not, allowing improved supervision of automated functions by flight crews.

Human-Autonomy Teaming↗

NASA NTRS2017

Application of Human-Autonomy Teaming to an Advanced Ground Station for Reduced Crew Operations

Within human factors there is burgeoning interest in the "human-autonomy teaming" (HAT) concept as a way to address the challenges of interacting with complex, increasingly autonomous systems. The HAT concept comes out of an aspiration to interact with increasingly autonomous systems as a team member, rather than simply use automation as a tool. The authors, and others, have proposed core tenets for HAT that include bi-directional communication, automation and system transparency, and advanced coordination between human and automated teammates via predefined, dynamic task sequences known as "plays." It is believed that, with proper implementation, HAT should foster appropriate teamwork, thus increasing trust and reliance on the system, which in turn will reduce workload, increase situation awareness, and improve performance. To this end, HAT has been demonstrated and/or studied in multiple applications including search and rescue operations, healthcare and medicine, autonomous vehicles, photography, and aviation. The current paper presents one such effort to apply HAT. It details the design of a HAT agent, developed by Human Automation Teaming Solutions, Inc., to facilitate teamwork between the automation and the human operator of an advanced ground dispatch station. This dispatch station was developed to support a NASA project investigating a concept called Reduced Crew Operations (RCO); consequently, we have named the agent R-HATS. Part of the RCO concept involves a ground operator providing enhanced support to a large number of aircraft with a single pilot on the flight deck. When assisted by R-HATS, operators can monitor and support or manage a large number of aircraft and use plays to respond in real-time to complicated, workload-intensive events (e.g., an airport closure). A play is a plan that encapsulates goals, tasks, and a task allocation strategy appropriate for a particular situation. In the current implementation, when a play is initiated by a user, R-HATS determines what tasks need to be completed and has the ability to autonomously execute them (e.g., determining diversion options and uplinking new routes to aircraft) when it is safe and appropriate. R-HATS has been designed to both support end users and researchers in RCO and HAT. Additionally, R-HATS and its underlying architecture were developed with generalizability in mind as a modular software applicable outside of RCO/aviation domains. This paper will also discuss future further development and testing of RHATS.

automation↗

NASA NTRS2017

A Human-Autonomy Teaming Approach for a Flight-Following Task

Managing aircraft is becoming more complex with increasingly sophisticated automation responsible for more flight tasks. With this increased complexity, it is becoming more difficult for operators to understand what the automation is doing and why. Human involvement with increasingly autonomous systems must adjust to allow for a more dynamic relationship involving cooperation and teamwork. As part of an ongoing project to develop a framework for human-autonomy teaming (HAT) in aviation, a part-task study was conducted to demonstrate, evaluate and refine proposed critical aspects of HAT. These features were built into an automated recommender system on a ground station available from previous studies. Participants performed a flight-following task once with the original ground station (i.e., No HAT condition) and once with the HAT features enabled (i.e., HAT condition). Behavioral and subjective measures were collected; subjective measures are presented here. Overall, participants preferred the ground station with HAT features enabled compared to the station without the HAT features. Participants reported that the HAT displays and automation were preferred for keeping up with operationally important issues. Additionally, participants reported that the HAT displays and automation provided enough situation awareness to complete the task and reduced workload relative to the No HAT baseline.

ACFP↗

NASA NTRS2017

A Human-Autonomy Teaming Approach for a Flight-Following Task

Human involvement with increasingly autonomous systems must adjust to allow for a more dynamic relationship involving cooperation and teamwork. As part of an ongoing project to develop a framework for human autonomy teaming (HAT) in aviation, a study was conducted to evaluate proposed tenets of HAT. Participants performed a flight-following task at a ground station both with and without HAT features enabled. Overall, participants preferred the ground station with HAT features enabled over the station without the HAT features. Participants reported that the HAT displays and automation were preferred for keeping up with operationally important issues. Additionally, participants reported that the HAT displays and automation provided enough situation awareness to complete the task, reduced the necessary workload and were efficient. Overall, there was general agreement that HAT features supported teaming with the automation. These results will be used to refine and expand our proposed framework for human-autonomy teaming.

Human-Autonomy Teaming (HAT)↗

NASA NTRS2017

Developing a General Framework for Human Autonomy Teaming

Automation has entered nearly every aspect of our lives, but it often remains hard to understand. Why is this? Automation is often brittle, requiring constant human oversight to assure it operates as intended. This oversight has become harder as automation has become more complicated. To resolve this problem, Human-Autonomy Teaming (HAT) has been proposed. HAT looks to make automation act as more of a teammate, by having it communicate with human operators in a more human, goal-directed, manner which provides transparency into the reasoning behind automated recommendations and actions. This, in turn, permits more trust in the automation when it is appropriate, and less when it is not, allowing a more targeted supervision of automated functions. This paper proposes a framework for HAT, incorporating two key tenets: bi-directional communication, and operator directed authority. We have successfully applied these tenets to integrating the autonomous constrained flight planner (an aide for planning diverts) into a dispatch station. We propose the development of general design patterns that may allow these results to be generalized to domains such as photography and automotive navigation. While these domains are very different, we find application of our HAT tenets provides a number of opportunities for improving interaction between human operators and automation.

human factors↗

NASA NTRS2017

Reduced Crew Operations Research at NASA Ames Research Center

In 2012, NASA began exploring the feasibility of single pilot reduced crew operations (SPORCO) in the context of scheduled passenger air carrier operations (i.e., Parts 121 and 135). This research was spurred by two trends in aviation research: the trend toward reducing costs and a shortage of pilots. A series of simulations were conducted to develop tools and a concept of operations to support RCO. This slide deck is a summary of the NASA Ames RCO research prepared for an R T team at Airbus. Airbus is considering moving forward with reducing crew during the cruise phase of flight with long-haul flights and is interested in the work we have completed.

reduced crew operations↗

NASA NTRS2017

Understanding Human Autonomy Teaming Through Applications

This presentation describes the development and demonstration of human autonomy teaming technologies for improving aviation safety and efficiency during nominal and off-nominal operations by developing and validating increasingly autonomous systems concepts, technologies, and procedures.

Aponso, B.↗

NASA NTRS2016

Application of Human-Autonomy Teaming (HAT) Patterns to Reduced Crew Operations (RCO)

As part of the Air Force - NASA Bi-Annual Research Council Meeting, slides will be presented on recent Reduced Crew Operations (RCO) work. Unmanned aerial systems, robotics, advanced cockpits, and air traffic management are all examples of domains that are seeing dramatic increases in automation. While automation may take on some tasks previously performed by humans, humans will still be required, for the foreseeable future, to remain in the system. The collaboration with humans and these increasingly autonomous systems will begin to resemble cooperation between teammates, rather than simple task allocation. It is critical to understand this human-autonomy teaming (HAT) to optimize these systems in the future. One methodology to understand HAT is by identifying recurring patterns of HAT that have similar characteristics and solutions. A methodology for identifying HAT patterns to an advanced cockpit project is discussed.

Human-Autonomy Teaming (HAT)↗

NASA NTRS2016

Application of Human-Autonomy Teaming (HAT) Patterns to Reduce Crew Operations (RCO)

Unmanned aerial systems, robotics, advanced cockpits, and air traffic management are all examples of domains that are seeing dramatic increases in automation. While automation may take on some tasks previously performed by humans, humans will still be required, for the foreseeable future, to remain in the system. The collaboration with humans and these increasingly autonomous systems will begin to resemble cooperation between teammates, rather than simple task allocation. It is critical to understand this human-autonomy teaming (HAT) to optimize these systems in the future. One methodology to understand HAT is by identifying recurring patterns of HAT that have similar characteristics and solutions. This paper applies a methodology for identifying HAT patterns to an advanced cockpit project.

design patterns↗

NASA NTRS2015

Pilot Situation Awareness and Its Implications for Single Pilot Operations: Analysis of a Human-In-The-Loop Study

In 2012, NASA began exploring the feasibility of single pilot/reduced crew operations in the context of scheduled air carrier operations. The current study examined how important it was for ground-based personnel providing support to single piloted aircraft (ground operators) to have opportunities to acquire situation awareness (SA) prior to being called on to assist an aircraft. We looked at two distinct concepts of operation, which varied in how much information was available to ground operators prior to being called on to assist a critical event (no vs. some Situation Preview). Thirty-five commercial pilots participated in the current study. Results suggested that a ground operators’ lack of initial SA when called on for dedicated assistance is not an issue, at least when the ground operator station displays environmental and systems data which are important to gaining overall SA of the specified aircraft. With appropriate displays, ground operators were able to provide immediate assistance, even if they had minimal SA prior to getting a request.

Reduced crew operations↗

NASA NTRS2014

Toward Single Pilot Operations: The Impact of the Loss of Non-Verbal Communication on the Flight Deck

Since the 1950s, the crew required to fly transport category aircraft has been reduced from five to two. NASA is currently exploring the feasibility of a further reduction to one pilot. In this study we examine the effects of separating the pilots on crew interaction. The results are consistent with earlier research on decision-making between remote groups. Pilots strongly prefer face-to-face interactions; however, we could find no impact of separation on their ultimate decisions. There were a number of areas in which separation negatively affected communications. We discuss possible mitigations for these areas.

Single Pilot Operations↗

NASA NTRS2012

An Evaluation of Controller and Pilot Performance, Workload and Acceptability under a NextGen Concept for Dynamic Weather Adapted Arrival Routing

In todays terminal operations, controller workload increases and throughput decreases when fixed standard terminal arrival routes (STARs) are impacted by storms. To circumvent this operational constraint, Prete, Krozel, Mitchell, Kim and Zou (2008) proposed to use automation to dynamically adapt arrival and departure routing based on weather predictions. The present study examined this proposal in the context of a NextGen trajectory-based operation concept, focusing on the acceptability and its effect on the controllers ability to manage traffic flows. Six controllers and twelve transport pilots participated in a human-in-the-loop simulation of arrival operations into Louisville International Airport with interval management requirements. Three types of routing structures were used: Static STARs (similar to current routing, which require the trajectories of individual aircraft to be modified to avoid the weather), Dynamic routing (automated adaptive routing around weather), and Dynamic Adjusted routing (automated adaptive routing around weather with aircraft entry time adjusted to account for differences in route length). Spacing Responsibility, whether responsibility for interval management resided with the controllers (as today), or resided with the pilot (who used a flight deck based automated spacing algorithm), was also manipulated. Dynamic routing as a whole was rated superior to static routing, especially by pilots, both in terms of workload reduction and flight path safety. A downside of using dynamic routing was that the paths flown in the dynamic conditions tended to be somewhat longer than the paths flown in the static condition.

Johnson, Walter W.↗

NASA NTRS2012

Comparison of Controller and Flight Deck Algorithm Performance During Interval Management with Dynamic Arrival Trees (STARS)

Managing the interval between arrival aircraft is a major part of the en route and TRACON controller s job. In an effort to reduce controller workload and low altitude vectoring, algorithms have been developed to allow pilots to take responsibility for, achieve and maintain proper spacing. Additionally, algorithms have been developed to create dynamic weather-free arrival routes in the presence of convective weather. In a recent study we examined an algorithm to handle dynamic re-routing in the presence of convective weather and two distinct spacing algorithms. The spacing algorithms originated from different core algorithms; both were enhanced with trajectory intent data for the study. These two algorithms were used simultaneously in a human-in-the-loop (HITL) simulation where pilots performed weather-impacted arrival operations into Louisville International Airport while also performing interval management (IM) on some trials. The controllers retained responsibility for separation and for managing the en route airspace and some trials managing IM. The goal was a stress test of dynamic arrival algorithms with ground and airborne spacing concepts. The flight deck spacing algorithms or controller managed spacing not only had to be robust to the dynamic nature of aircraft re-routing around weather but also had to be compatible with two alternative algorithms for achieving the spacing goal. Flight deck interval management spacing in this simulation provided a clear reduction in controller workload relative to when controllers were responsible for spacing the aircraft. At the same time, spacing was much less variable with the flight deck automated spacing. Even though the approaches taken by the two spacing algorithms to achieve the interval management goals were slightly different they seem to be simpatico in achieving the interval management goal of 130 sec by the TRACON boundary.

Battiste, Vernol↗

NASA NTRS2012

Task Allocation for Single Pilot Operations: A Role for the Ground

Researchers at NASA Ames Research Center and NASA Langley Research Center are jointly investigating issues associated with potential configurations for an environment in which a single pilot, or reduced crew, might operate. The research summarized in this document represents several of the efforts being put forth at NASA Ames Research Center. Specifically, researchers at NASA Ames Research Center coordinated and hosted a technical interchange meeting in order to gain insight from members of the aviation community. A description of this meeting and the findings are presented first. Thereafter, plans for ensuing research are presented.

Johnson, Walter↗

NASA NTRS2011

Application of Human-Autonomy Teaming (HAT) Patterns to Reduce Crew Operations (RCO)

Unmanned aerial systems, advanced cockpits, and air traffic management are all seeing dramatic increases in automation. However, while automation may take on some tasks previously performed by humans, humans will still be required to remain in the system for the foreseeable future. The collaboration between humans and these increasingly autonomous systems will begin to resemble cooperation between teammates, rather than simple task allocation. It is critical to understand this human-autonomy teaming (HAT) to optimize these systems in the future. One methodology to understand HAT is by identifying recurring patterns of HAT that have similar characteristics and solutions. This paper applies a methodology for identifying HAT patterns to an advanced cockpit project.

design patterns↗

NASA NTRS2009

Toward Online Measurement of Decision State

In traditional perceptual decision-making experiments, two pieces of data are collected on each trial: response time and accuracy. But how confident were participants and how did their decision state evolve over time? We asked participants to provide a continuous readout of their decision state by moving a cursor along a sliding scale between a 100% certain left response and a 100% certain right response. Subjects did not terminate the trials; rather, trials were timed out at random and subjects were scored based on the cursor position at that time. Higher rewards for correct responses and higher penalties for errors were associated with extreme responses so that the response with the highest expected value was that which accurately reflected the participant's odds of being correct. This procedure encourages participants to expose the time-course of their evolving decision state. Evidence on how well they can do this will be presented.

Lachter, Joel↗

NASA NTRS2009

Toward Online Measurement of Decision State

In traditional perceptual decision-making experiments, two pieces of data recollected on each trial: response time and accuracy. But how confident were participants and how did their decision state evolve over time? We asked participants to provide a continuous readout of their decision state by moving a cursor along a sliding scale between a 100% certain left response and a 100% certain right response. Subjects did not terminate the trials; rather, trials were timed out at random and subjects were scored based on the cursor position at the time. Higher rewards for correct responses and higher penalties for errors were associated with extreme responses so that the response with the highest ex[pected value was that which accurately reflected the participant's odds of being correct. This procedure encourages participants to expose the time-course of their evolving decision state. Evidence on how well they can do this will be presented.

Lachter, Joel↗