quarta-feira, 5 de janeiro de 2022

EYE TRACKING IN FLIGHT DECK OPERATIONS – UPCOMING FLIGHT SAFETY DEVICE: COCKPIT MONITORING THE PILOT’S EYES

 


ABRIDGE

Eye tracking metrics for workload estimation in flight deck operations

Kyle Kent Edward Ellis

University of Iowa

Reference:

Operator Performance Laboratory, NASA, Smarteye Inc.

University of Iowa

Defence Research and Development Canada

Scientific Report

 

The eye movements of aircrew during flight have been a topic of interest to military and civilian flights. Studies in flight simulators and real aircraft have used eye movements as a window onto operators’ processing of information from cockpit instruments and displays.

The interaction between the operator and the aircraft interface

Analysis of operator state in different testing scenarios in flight deck operations.

Determining workload fixation duration and blink rate

Current avionics are not aware of pilot real-time capabilities and limitations resulting from varying workload levels.

The concept of the intelligent flight deck is currently being pursued by the National Aeronautics and Space Administration (NASA), with specific interest in characterizing operator state in flight deck operations. The goal is to use operator workload and overall cognitive state effectively to optimize the flight deck interface.

Basic visual search is comprised of two components:

Fixations and

Saccadic movements.

A fixation is a set of look-points or a series of eye gaze vector data points that is focused on a stationary target in the person’s visual field (Applied Science Laboratories, 2007).

A fixation is the duration of time for which an individual is visually collecting and interpreting whatever information is available within the foveal range of the eye.

When the fixation is made on a point close to the individual, such as on a flight deck, visual angle decreases significantly depending on the distance from the eye. The central 1.5 degrees of visual field have a visual resolution many times greater than that of the peripheral vision (Rao, Zelinsky, Hayhoe, & Ballard, 1997).

This region of resolution is the only field in which the eye is capable of interpreting fine resolution information, such as words in a book.

Converting the reading information analogy to that of heads down displays on a flight deck, the highest resolution necessary of any eye tracker needs to be at least within two degrees visual angle (Rayner & Bertera, 1979). Various components of eye fixations are the duration, the frequency, and the location in which they are made.

The eye movement from one fixation to the next is called a saccade.

A saccade connects one fixation to the next, and can be measured in terms of radial degrees.


Different components to a saccade include the length of the saccade (visual angle), the speed of the saccade in degrees per second, and the direction of the saccade.

When reading, the eye makes rapid movements, as many as four to five per second, moving from one fixation to the next, focusing on a few words each time (Rayner & Bertera, 1979).

The eye does not transmit visual signals to the brain when making a saccade.

A saccade is made each time information is obtained from one fixation and another fixation is necessary to observe further information elsewhere.

Combining saccadic movements and their associated fixations a scan pattern or scan path emerges.

The quality of the eye tracking itself is not affected by differences among pilots’ Experience.

Experienced pilots will typically be more comfortable while performing a flight task with a basic knowledge of what they need to look at to obtain the information they need. This increases the efficiency of their eye behavior, resulting in a difference in eye tracking metrics in contrast to a novice pilot.

Pupil color greatly impacts the quality of eye tracking for many eye trackers.

High precision eye trackers require a sharp contrast between the pupil and the iris. Bright pupil systems require direct infrared reflection off of the retina therefore, subjects with blue eyes are often times easier to track. This is due to blue eyes containing less IRreflective melanin in the iris. In contrast to this, brown or hazel eyes are usually ideal for eye tracking systems that utilize a dark pupil contrast. (Boyce, Ross, Monaco, Hornak, & Xin, 2006); (Wang, Lin, Liu, & Kang, 2005).

Pilots who may be sleep deprived also pose another form of problem. Eyelid closure can become an issue when the eyelid itself begins to cover portions of the pupil.

Corrective lenses, such as glasses, pose reflection issues that pose as the biggest threat to eye tracking quality. Lenses posing the largest problem are lenses with hard edged bi- or tri-focal lenses due to distortion of the eye image as seen from the perspective of the eye tracking cameras. Distortions typically occur due to lens shape, causing problems with systems using corneal reflection, bright retinal reflection, dark pupil circle, limbus or iris features, etc.

Soft contact lenses typically do not cause problems however, hard contacts can cause edge problems in bright pupil systems typically caused by dirt or dust trapped beneath the lens.

Typically single vision corrective eye glasses do not cause problems unless they have an anti-reflective coating.

Lenses with curved front surfaces will often times because of problems caused by reflecting the infrared source back into the camera.

Three theories of eye tracking data analysis (Jacob & Karn, 2003):

1.  Top-down based on cognitive theory: “Longer fixations on a control element in the interface reflect a participant’s difficulty interpreting the proper use of that control.”

Top-down based on a design hypothesis: “People will look at a banner advertisement on a web page more frequently if we place it lower on the page.”

Bottom-up: “Participants are taking much longer than anticipated making selection on this screen. We wonder where they are looking.”

Post-run analysis can lead to indications of why a subject, in this case a pilot, would spend more time on the attitude indicator than the airspeed indicator, both of which are of high importance. The answers to such questions can lead to further understanding of pilot workload, and what is consuming their cognitive capacity and why.

Eye tracking data:

Average Dwell Time – The total time spent looking at an instrument divided by the total number of individual dwells on that instrument.

Dwell percentage – Dwell time on a particular instrument as a percent of total scanning time.

Dwell Time – The time spent looking within the boundary of an instrument.

Fixation – A series of continuous look points which stay within a pre-defined radius of visual degrees.

Fixations per dwell – The number of individual fixations during an instrument dwell.

Glance – A “subconscious” (i.e., non-recallable) verification of information with a duration histogram peaking at 0.1 seconds. (also referred to as an “orphan”)

Lookpoint – The current coordinates of where the pilot is looking, frequency of data points depending on the eye tracking system used.

One-way transition – The sum of all transitions from one instrument to another (one direction only) in a specified instrument pair.

Out of track – A state in which the eye tracking system cannot determine where the pilot is looking, such as during a blink or when  the subject’s head movement has exceeded the tracking capabilities of the system setup.

Saccade – The movements of the eye from one fixation to the next. Also considered to be the spatial change in fixations.

Scan – Eye movement technique used to accomplish a given task. Measures used to quantify a scan include (but are not limited to) transitions, dwell percentages, and average dwell times.

Transition – The change of a dwell from one instrument to another.

Transition rate – The number of transitions per second.

Two-way transition – The sum of all transitions between an instrument pair, regardless of direction of the transition.

(Harris, Glover, & Spady, 1986)

Fixations

Eye fixations are defined as “a relatively stable eye-in-head position within some threshold of dispersion (~2 deg) over some minimum duration (200ms), and with a velocity threshold of 15-100 degrees per second” (Jacob & Karn, 2003). Several studies have been conducted utilizing eye fixation measures. The total number of fixations has been observed to correlate negatively with efficiency; however, efficiency is seen to correlate negatively with workload (Goldberg & Kotval, 1998).

Fixation frequency that shows a positive correlation to subject workload similar to fixation total. Fixation frequency has shown to indicate more effortful search, indicating poor performance accuracy and longer search times in memory tasks (Van Orden, Limbert, Makeig, & Jung, 2001).

Fixation duration, including the mean and maximum duration, indicates increased workload in flight.

Longer fixations are indicative of increases in cognitive processing loads during a period of time (Callan, 1998).

Gaze

Very similar to the fixation metric, gaze analyzes the grouping of fixations within a single region of interest.

Analysis of the gaze metric, including gaze rate (# of gazes / minute) on each area of interest, gaze duration mean and gaze percentage (proportion of time) in each area of interest for 40 pilots flying an  aircraft landing approach (Fitts, Jones, & Milton, 1950). Gaze metrics focus more on the area of interest and what it represents, not only the measure of a fixation in any given region of space.

Saccadic Movement

Measures of saccadic movement are often times neglected in usability research initiatives because many of its close relation to fixations measures, which are easier to examine are used instead.

The length of the saccade, as well as the speed of which the saccade is made are both very easily calculated measures, simply calculating the distance from one fixation to the next in an ordered pair.

The frequency of longer length saccadic movements could indicate a correlation of decreased efficiency, and potentially an increase in perceived workload.

Scan-Path

Several research studies have been conducted that analyze scan-path as it relates to efficiency, workload, usability, effectiveness, effort, saliency, and other forms of human factors.

Scan-path is often looked at as the measurable window that depicts how a subject uses their visual sensory perception to complete any task at hand, carrying with it also the distractions and other important artifacts that are included that add or detract to an individual’s intention of completing that task. Scan-path analysis measures the transitions between fixations, including measures of transitions between areas of interest (link-analysis) as a quantifiable measure.

It is particularly useful in bottom-up analysis approaches that seek to identify where someone is looking and why, in an attempt to understand the cognitive background to an individual’s eye tracking behavior.

From a top-down approach scan-path is seemingly less useful.

Blink Rate

Research using air traffic controllers in high and low workload situations suggests that increases in workload negatively correlate with blink rate (Brookings, Wilson, & Swain, 1996); (Wilson, Purvis, Skelly, Fullenkamp, & Davis, 1987).

The fundamental belief being that workload is higher requiring more focused attention and a general increase in visual load. Blinks therefore occur less often so it is less likely to miss critical information. This requires the amount of time the eye is collecting  information to be increased thereby resulting in a decreased blink rate (Brookings, Wilson, & Swain, 1996).

Fixation Maps

Fixation mapping is the “analysis of eye-movement traces” of a given scene.

Example of Fixation Map on Standard 737 EFIS PFD

Example of Fixation Map on Standard 737 EFIS PFD 2

When analyzing fixation maps it is not the analysis of fixation order, but the location of the fixation that is important.

BOEING 737-800 FLIGHT DECK EYE TRACKING RESEARCH STUDY

A complex flight task that will yield a wide variation in relative physical and cognitive workload levels.

This will be used to observe pilot’s eye movement behavior under these varying conditions. From this it will show that eye movement measures are affected by task loading.


Smarteye Eye Tracking System

The 737 flight deck utilized a 3 camera system to achieve the visual angle of eye tracking necessary to capture the test pilots’ gaze across the flight deck areas of interest.


To obtain quantitative eye tracking data, a Smarteye eye tracking system was installed and optimized inside the OPL’s 737-800 simulator. The Smarteye eye tracker is a remote eye tracking system that uses facial recognition to calculate the position of defined points on a subjects head relative to the calibrated position of 2 or more cameras.

The camera’s use the facial features to locate the corners of each of the subject’s eyes and digitally zooms to enhance the image of the eye.

To calculate eye gaze vectors from the head origin, infrared led’s project infrared light onto the pilots face, illuminating the pilots face as well as creating two ocular reflections; a static corneal reflection and a moving pupil reflection that moves in conjunction with eye movements. By triangulating the angular difference between the corneal reflection and pupil reflection, the Smarteye eye tracking system can create a vector between the two points to create an eye gaze vector originating from the corneal reflection at the center of the subject’s eyes.

Practicing in simulator B737-800

KORD Runway 9R Approach to Land Task

 

The main objective to the design of the experiment was to develop a series of flight scenarios that utilized the same flight task but could demand several different levels of workload from the pilot. To accomplish this, a single approach task to Chicago O’Hare International airport was chosen.

 The initial point (IP) started the flight test simulation southwest of the DPA VOR at 10,000 feet. Pilots were contacted by Chicago center and instructed to descend to 7000 feet and maintain 200 knots on course to DPA. Approximately 5 NM out from DPA pilots were instructed by Chicago center to contact Chicago approach at radio frequency

119.0. Once contact with Chicago approach was established, pilots were instructed to descend to 6000 feet, continue to waypoint Burke and establish the aircraft on the localizer cleared for runway 9R. Pilots then proceeded to follow the flight plan to waypoints Pratt and Carle. Approximately 1 NM from waypoint Deana pilots were instructed by O’Hare approach to contact O’Hare tower at radio frequency 121.75. With the aircraft inside the outer marker of O’Hare, pilots were cleared to land by the tower.

The flight test engineer in the right seat of the flight deck was responsible for making calls to decision height at 1000, 500 and 200 feet to minimums. Upon reaching decision height pilots were expected to make a land or go around call and execute the procedure depending on visual acquisition of the REILs.

The KORD runway 9R approach task includes five waypoints with designated speeds and altitudes pilots were instructed to establish upon reaching that given waypoint:

• DPA – 200 knots at 7000 MSL

• Burke – 180 knots at 6000 MSL

• Pratt – 165 knots at 5000 MSL

• Carle – 165 knots at 4000 MSL

• Deana – 145 knots at 2300 MSL


Pilots were instructed to maintain a sterile cockpit, keeping verbal communication to a minimum during each test run, speaking only during radio calls and workload callbacks. A checklist was provided to the pilots listing each waypoint and the speeds and altitudes they are to establish upon arrival at each waypoint. Also provided for each waypoint were suggested flap positions, MCP engage commands, and gear down instructions.



The KORD ILS runway 9R approach plate was also provided to the pilots as a standard in flight reference of the approach task. The ILS approach plates give information to pilots in a familiar form to pilots with an IFR and above license. It lists the typical clearance altitudes to be expected and distances between approach waypoints, as well as radio frequencies of O’Hare approach and O’Hare towers. This information could be used by the pilot to pre-program the radios if so desired to make the flight tasks easier when asked to transfer radio contact to approach or tower. The approach plate was available for all test runs and was the basis for programming the flight plan into the FMS.




Test Conditions

Two methods to drive workload to show high-low workload contrasts were implemented:

 1. Visibility condition – CAT II and CAT III o Land or Go Around condition

 2. Level of automation

®     Full Autopilot and Auto-throttle

®     FD guidance and Auto-throttle

®     Manual approach with localizer course and glide slope guidance only

 For the visibility condition, outside visuals were controlled to be set to CAT II visibility, with no greater than 0.3 NM visibility, or set to CAT III, with no greater than 0.1 NM visibility. The threshold of visibility between the two visibility conditions forced the pilot to make a land-no land decision at decision height at 200 feet AGL. Upon reaching 200 feet AGL, federal air regulations (FARs) state that the pilot must be able to see the runway end indicator lights (REILs) to continue to land. If the pilot cannot see the REILs at 200 feet above the runway, the pilot must execute a go-around. If the pilot is able to see the REILs at 200 feet AGL, then the pilot was to proceed to 100 feet AGL where they are required by FARs to make visual contact with the end of the runway to continue to land. The variance in visibility conditions made no impact on the 100 foot AGL decision height. The difficulty for the pilot is found in the time for which the decision to land must be made and to maintain the proper flight path to the runway with no outside visuals obtaining guidance strictly from the HDDs.

Several times pilots would find themselves off-course due to the high mental demand required by several systems at once, such as thrust level, attitude adjustment and radio communication. Diverging oscillations in recovering the flight path intercept was very typical across pilots in the manual condition, ultimately driving the pilots’ workload to higher levels on the Bedford workload scale.









Workload

Of course, pilot errors and performance decrements can result from causes beyond a loss of SA. (Lefrancois, Matton, Gourinat, Peysakhovich, and Causse(2016). Named automation addiction due to pressure and fatigue as a factor leading to errors in monitoring flight instruments.

They examined 20 pilots who were instructed to land an Airbus A320 manually in a flight simulator.

 A quarter of the pilots was unable to stabilize the aircraft and made the decision to go around. The authors concluded that gaze patterns for these pilots were suboptimal in comparison to those of the pilots who stabilized and landed the aircraft most precisely.

 They did not sufficiently scan primary flight instruments to fly the approach. The authors assumed that these pilots were not sufficiently trained to fly manual approaches.

 In a closely related experiment, compared pilot groups who inappropriately flew on during an ill-advised approach and those who appropriately decided to go around. They observed differences in visual allocation of attention between the two groups, as well as differences between the pilot flying and the copilot navigating. The latter differences were quantified and reflected both in where the two types looked inside and outside the cockpit, and also in the qualitative style of eye movements. (Dehais, Behrend, Peysakhovich, Causse, and Wickens(2017).







 



quarta-feira, 1 de dezembro de 2021

Analysis of the Vertical Navigation (VNAV) Function - Pilot’s reports about VNAV issues

 




SOURCES:


Lance Sherry
RAND Honeywell International, Inc.
Phoenix, Arizona

Michael Feary
NASA Ames Research Center
Moffett Field, California

Peter Polson
Department of Psychology
University of Colorado, Boulder, Colorado

Randall Mumaw
Boeing - Commercial Airplane Group
Seattle, Washington

Everett Palmer
NASA Ames Research Center
Moffett Field, California

Cockpit automation is to use automation to build intelligent agents that automate operator tasks.

 

"To command effectively, the human operator must be involved and informed. Automated systems need to be predictable and capable of being monitored by human operators. Each element of the [cockpit] system must have knowledge of the other's intent." This is the spirit of the guidelines developed by Billings for human-centered automation. (Billings, C. E. (1997).

 

Pilots generally use the VNAV function during the climb and cruise phases of flight.


In a survey of 203 pilots (questionnaire at the end of this post) at a major U.S. airline, McCrobie et al., found that:

 - 73% of pilots used VNAV in climb phase;

 - 20% used the function in descent; and,

 - 5% use the function in approach.


The VNAV function (also known as the PROF function) accounts for the majority of reported human factor issues with cockpit automation. 


Vakil & Hansman's review of Aviation Safety Reporting System (ASRS) reports, an anonymous incident  reporting data-base for pilots, found that 63% of pilot-cockpit interaction issues were in the control of the coupled vertical/speed trajectory of the aircraft performed by the VNAV function.

 

Each new generation of aircraft has increasing levels of flight deck automation that have improved the safety and efficiency of airline operations. The full potential of these technologies has not been fully realized however. A case in point is the potential to improve operations during the workload-intensive descent and approach phases of flight. The Vertical Navigation (VNAV) function of the Flight Management System (FMS) serves as an intelligent agent during these phases by automatically selecting appropriate targets (e.g. altitude, speed, and vertical speed) and pitch/thrust control modes to satisfy the objectives of each leg of the flightplan. This decision-making logic is complex and has raised several sets of human factors related concerns. 


A cognitive engineering analysis of the NASA Research VNAV function (representative of the PROF function on Airbus aircraft and the VNAV functions in modem Boeing airplanes) identified that the current design of the user interface for the VNAV function violates two basic principles of cognitive engineering for interfaces between operators and complex automation:

 

1. The VNAV button is overloaded in descent and approach phases of flight.

 

Selecting the VNAV button results in the engagement one of six possible VNAV commanded trajectories.

 

2. Flight Mode Annunciator (FMA) for the VNAV function is overloaded in descent and approach phases of flight.


The same FMA is used to represent different trajectories commanded by the VNAV function.

 

Overloading of user-interface input devices and overloading of display feedback are well known sources of operator confusion. 


These principles are considered to contribute directly to the difficulties pilots have in learning, understanding, and predicting complex automation behavior. 


The airlines are effectively relying on the pilot community to discover and informally communicate to each other ways of using the function in all flight regimes. This is reflected in a series of surveys that found that pilots request additional training on VNAV and other FMS functions over all other aircraft systems.

 

The VNAV function provides three automated features:

 

1. VNAV automatically selects altitude targets and speed targets according to pilot MCP entries and the altitude and speed constraints in the FMS flightplan.

 

2. VNAV automatically selects pitch and thrust control modes to fly to the targets.


For example during descent, VNAV chooses between a FLCH descent, a vertical speed (fixed rate-of descent), and an FMS path descent. In the case where VNAV selects vertical speed control mode, VNAV also selects the vertical speed target. 


3. For the descent and approach, VNA V automatically provides an optimum path that is used as the reference for all automated altitude/speed target and control mode selections.

   

Automated selection of VNAV targets 


A study of the soft-ware of contemporary VNAV functions, Sherry & Poison, found that the typical VNAV function automatically chooses the active altitude target from a possible list of sixteen, and chooses the active speed target from a possible list of twenty-six. Pilots are generally familiar with only a small set of these targets that occur most frequently and are self-explanatory.

 

For example, the VNAV altitude target is almost always the pilot entered MCP altitude. In rare cases, when the MCP altitude has been raised above a constraint altitude in the climb phase of the FMS flightplan (or lowered below a constraint altitude in the descent phase of the FMS flightplan), the VNAV function will capture and maintain the constraint altitude (and not the MCP Altitude). Hutchins  describes scenarios in which pilots became confused with the relationship between the MCP altitude and the FMS flightplan altitude. 


The remaining altitude targets automatically selected by VNAV cover "comer cases" and are rarely observed during revenue service operations. For example, the VNAV function will automatically level the aircraft off if there is a conflict between the direction of the pilot entered MCP altitude and the phase of the flightplan. Dialing the MCP altitude below the aircraft in the climb phase of the flightplan results in an immediate level off. Other unusual altitude targets include; an intermediate level-off at 10,000 feet during descent to bleed off speed to satisfy the 10,000ft/250kt. restriction, an intermediate level-off to intercept the glideslope, or when the aircraft has descended below the Minimum Descent Altitude (MDA) on a non-precision approach.

 

3 keys to demystifying VNAV selection of targets 


First a deep understanding of the FMS flightplan and,


Second how the altitude and speed constraints are used to determine targets is required.

 

This must be coupled with knowledge of the dynamic relationship between the MCP and the FMS flightplan for selecting targets. 


Third, the "comer case" targets of the VNAV function must be understood.



Automated selection of VNAV pitch/thrust control modes 


Automated mode selection by the VNAV function of pitch/thrust control modes can be confusing in two ways. The most common source of confusion is the autonomous transition of the mode without pilot action. These "silent" mode transitions are made when VNAV detects that certain criteria have been satisfied. For example, when the aircraft speed exceeds a threshold (typically 20 knots) above the FMS path speed, VNAV will autonomously switch control modes from VNAV-PATH to VNAV SPEED. These thresholds are generally not annunciated on cockpit displays. 


The second source of confusion is the selection of control modes made by VNAV given the circumstances of the aircraft. For example, several pilots prefer to perform descents to crossing restrictions with a FLXed rate of descent (i.e. vertical speed mode). By triangulating time (or distance) to the waypoint and remaining altitude, pilots can ensure making the restriction. In certain circumstances VNAV will choose speed-on pitch with idle thrust and request airbrakes to make the restriction. 


The key to understanding the choice of control modes made by the VNAV function is to understand the overall FMS philosophy on how descents are flown.

 

Automatic use of FMS optimum path as a reference 


One of the biggest contributors to pilot confusion with VNAV is the FMS computed optimum path. The path, computed by the FMS using models of aircraft performance, takes into account the regulations and constraints of standard arrival procedures (STARs) and published approaches. The nuances of the path, such as how far way from waypoints deceleration as reinitiated, are non-intuitive and worse not displayed in the cockpit.

 

When the aircraft is capturing and maintaining the path, the aircraft altitude control is earth-referenced with the goal of placing the aircraft 50-ft above the runway threshold. This operates much like the glide slope except that the reference be a misprovided by the FMS, not a ground-base transmitter. Unlike other up-and- way control modes, the aircraft will maintain the path without drift in the presence of wind. 


When the FMS optimum path is not constrained by crossing restrictions and appropriate wind entries have been made, the aircraft will descend at the desired speed with the throttles at idle. When the path is constrained or wind entries are sufficiently inaccurate, speed must be maintained using throttles (for underspeed) and airbrakes (for overspeed).

 

This "earth-referenced" control of altitude has been observed to confuse pilots who, on request from ATC to expedite the descent, add thrust or extend airbrakes. Because VNAV is controlling to the path, these actions simply increase or decrease speed without any effect on aircraft rate-of-descent.

 

The key to understanding the VNAV behavior in descent is to have full knowledge of the FMS optimum path. 


Pilots must understand the differences between airmass-referenced descents, such as FLCH, and earth referenced descents on the path. 


Pilots primarily monitor the behavior of the VNAV function by monitoring the trajectory of the aircraft. 


Pilots are "surprised" by the behavior of the VNAV function when the aircraft trajectory or the thrust indicators do not match their expectations. For example, when the aircraft vertical speed fails to decrease as the aircraft approaches an assigned altitude, pilots wonder whether the VNAV function is commanding a capture to the altitude. 


Secondary sources of information on VNAV include the Flight Mode Annunciator (FMA), targets on the Primary Flight Display (PFD) altitude tape and speed tape, and various MCDU pages (e.g. RTE/LEGS (or F-PLN), PROG page, CLB/CRZ/DES pages). 


Research Autopilot was demonstrated to be a source of pilot errors. This input device resulted in two different autopilot behaviors depending on the situation when it was selected. Selecting the vertical speed wheel: 


l. when the aircraft was outside the capture region, commanded the aircraft toffy to the assigned altitude (and armed the capture).

 

2. when the aircraft was inside the capture region, commanded the aircraft toffy away from the assigned altitude (and disarmed the capture) Frequently pilots were unaware of the dual nature of the vertical speed wheel, or could not distinguish between the dual "modes" of the wheel. As a result pilots were surprised by the behavior commanded by the autopilot. See also Palmer, Degani & Heymann, and NTSB.

 

There are six behaviors commanded by the VNAV function during the descent and approach phases of the flightplan, when the goal of VNAV is to DESCEND TO THE FINAL APPROACH FIX (FAF):

 

1. Descend on FMS Optimum Path 


2. Descend Return to Optimum Path from Long (Late) 


3. Descend Converge on Optimum Path from Short (Early) 


4. Maintain VNAV Altitude (i.e. altitude constraints, MCP altitude, or other VNAV altitudes) 


5. Descend Open to VNAV Altitude to Protect Speed 


6. Descend to VNAV Altitude, Hold to Manual Termination 


The basic underlying concept of the VNAV function is that the VNAV function constructs and strives to fly an optimum path to the FAF. This path is a geographically-fixed pathway from the cruise flight level to the runway that is designed to optimize fuel bum and time, and takes into account the altitude crossing restrictions, and speed and time constraints. It is flown in much the same way as the aircraft flies a glideslope beam. 


To stabilize the aircraft at the FAF the VNAV function commands trajectories to capture and maintain the path. The appropriate trajectories are determined by decision-making rules embedded in the software that take into account the position and speed of the aircraft relative to the path and other parameters. The VNAV function will automatically transition between commanded behaviors based on the situation perceived by the automation based on sensor data. 


For example, when the aircraft is commanded to initiate the descent before the optimal FMS computed Top-of-Descent, the VNAV function automatically commands a VNAV Behavior to Descend and Converge on the Optimum Path, usually with a fixed rate-of-descent. The rate-of-descent is selected such that the aircraft converges on the optimum path (Figure 2).





Alternatively, when the aircraft initiates the descent beyond the Top-of-Descent, the VNAV function automatically commands a VNAV Behavior to Descend and Return to Optimum Path. This VNAV behavior commands a descent at idle-thrust. Some VNAV functions increase the speed target to ensure convergence of the path (Figure 2).

 

Frequently the VNAV function determines that additional drag is required to converge on the optimum path and requests extension of the air-brakes via an ND and MCDU message.




Attitudes-Toward-Automation Questionnaire

 

Please indicate your agreement or disagreement with the following statements by circling the words that best describe your feelings:

 

1. I am concerned about a possible loss of my flying skills with too much automation.

 

Strongly Agree Agree Neutral Disagree Strongly Disagree

 

2. The automation in my current aircraft works great in today's ATC environment.

 

Strongly Agree Agree Neutral Disagree Strongly Disagree

 

3. I always know what mode the autopilot/flight director is in.

 

Strongly Agree Agree Neutral Disagree Strongly Disagree


4. I use the automation mainly because my company wants me to.

 

Strongly Agree Agree Neutral Disagree Strongly Disagree

 

5. Automation frees me of much of the routine, mechanical parts of flying so I can concentrate on "managing" the flight.

 

Strongly Agree Agree Neutral Disagree Strongly Disagree

 

6. In the automation of my current aircraft, there are still things that happen that surprise me.

 

Strongly Agree Agree Neutral Disagree Strongly Disagree

 

7. I make fewer errors in the automated airplanes than I did in the older models.

 

Strongly Agree ☐Agree Neutral Disagree Strongly Disagree

 

8. Automation helps me stay "ahead of the airplane".

 

Strongly Agree Agree Neutral Disagree Strongly Disagree

 

9. I spend more time setting up and managing the automation (CDU, FMS) than I would hand-flying or using a plain autopilot.

 

Strongly Agree Agree Neutral Disagree Strongly Disagree

 

10. Automation does not reduce total workload, because there is more to monitor now.

 

Strongly Agree Agree Neutral Disagree Strongly Disagree

 

11. I always consult the flight mode annunciator to determine which mode the autopilot/ flight director is in.

 

Strongly Agree Agree Neutral Disagree Strongly Disagree

 

12. Training for my current aircraft was as adequate as any training I have had.

 

Strongly Agree Agree Neutral Disagree Strongly Disagree

 

13. I use automation mainly because it helps me get the job done.

 

Strongly Agree Agree Neutral Disagree Strongly Disagree


14. It is easier to bust an altitude in an automated airplane than in other planes.

 

Strongly Agree Agree Neutral Disagree Strongly Disagree

 

15. Sometimes I feel more like a "button pusher" than a pilot.

 

Strongly Agree Agree Neutral Disagree Strongly Disagree

 

16. There are still modes and features of the autoflight system that I don't understand.

 

Strongly Agree Agree Neutral Disagree Strongly Disagre