Pilot Flying vs.
Pilot Monitoring during the approach
phase: an eye–tracking study
Maxime Reynal, Yvanne Colineaux, Andre Vernay, Frédéric Dehais
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Maxime Reynal, Yvanne Colineaux, Andre Vernay, Frédéric Dehais. Pilot Flying vs. Pilot Monitoring
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Reynal, Maxime and Colineaux, Yvanne and Vernay, Andre and Dehais, Frédéric Pilot Flying vs. Pilot Monitoring
during the approach phase: an eye–tracking study. (2016) In: HCI-Aero, 14 September 2016 - 16 September 2016
(Paris, France).
Pilot Flying vs. Pilot Monitoring during the approach
phase: an eye–tracking study
Maxime Reynal Yvanne Colineaux André Vernay Frédéric Dehais
Centre Aéronautique et Spatial Direction Générale de Direction Générale de Centre Aéronautique et Spatial
ISAE-SUPAERO l’Aviation Civile l’Aviation Civile ISAE-SUPAERO
University of Toulouse Paris, France Paris, France University of Toulouse
Toulouse, France yvanne.colineaux@aviation andre.vernay@aviation Toulouse, France
maxime.reynal@isae.fr -civile.gouv.fr -civile.gouv.fr frederic.dehais@isae.fr
ABSTRACT - The PM must inform the PF (or intervenes if necessary)
The adequate monitoring of the flight parameters in the of any deviation of the flight parameters, and executes
cockpit is a critical issue for flight safety. However, little is the PF’s orders.
known about how the crew supervises the flight deck. In
This distribution of roles emphasizes on the adequate
this paper, the preliminary results of a project dedicated to
monitoring of the cockpit, thus challenging pilots’
analyze pilot flying and pilot monitoring eyes movements
attentional abilities. However, it is well admitted that
collected in full flight simulator during approach phases are
attentional abilities are bounded. Recent studies have
presented. First analyses were conducted over 32 approach
shown that the occurrence of unexpected events is likely to
phases (8 different crews performing 4 approaches each).
capture human operator’s attention on a single task to the
The results revealed that the pilot flying and the pilot
detriment of the supervision of the flight [5] despite
monitoring exhibited similar ocular behavior during the
auditory alarms [6]. Operational fatigue and drowsiness
approach. Moreover, the findings suggested that the pilot
may lead to the state of mind wandering, leaving the pilot’s
monitoring’s attentional allocation may not be optimal
ill equipped to face complex and challenging situations
especially during the short final with low percentage of
when a problem arises [9]. As stated by [2]: “Real-world
dwell time on the speed indicator and high percentage of
monitors may be caught between a continuous vigilance
dwell time out of the window.
approach that is doomed to fail, a dynamic environment
Keywords that cannot be fully controlled, and what may be an
Flight safety, Eye-tracking, Human error, Pilot activity. irresistible urge to let one’s thoughts drift.” The National
Transportation Safety Board (NTSB) and the International
INTRODUCTION
Civil Aviation Organization (ICAO) stated that poor
Operating an aircraft is a complex activity that requires monitoring issues were involved in most of major civilian
efficient teamwork between the Captain and the First accidents, such as Colgan Air Flight 3407 [15], Asiana Air
Officer (F/O). During flight operations, the two Flight 214 [14], Turkish Airlines Flight 1951 [10], Air Inter
crewmembers can be alternatively either “Pilot Flying” Flight 148 or more recently the UPS Airlines Flight 1354
(PF) or “Pilot Monitoring” (PM). Their roles as PF and PM [13] to name a few [4]. The growing awareness of the need
are defined by the Standard Operating Procedures (SOPs) to better understand these events motivated the creation of
[18] : the Active Pilot Monitoring Group. This group identified
several contributive factors such as time pressure, human
- The PF is responsible for managing the aircraft flight limitation, poor mental models, automated flight deck
path and gives orders to the PM. issues and a “corporate climate that does not emphasis on
- The PM is responsible for monitoring the current and monitoring”. This work resulted in the “Practical Guide for
projected trajectory, the status (e.g. flight path, energy) Improving Flight Path Monitoring” that proposes several
of the aircraft and all external hazards (e.g. countermeasures to crew’s poor monitoring [11].
cumulonimbus). However, little is known on how the crewmember actually
monitors the flight deck and cross-check the flight
parameters especially during critical phases such as the
approach [3]. To that end, measuring eyes movements with
eye-tracking technique offers promising perspective to
undercover pilots’ voluntary or not attentional strategies.
Several studies revealed the suitability of the eye tracking
technique for understanding attentional vulnerabilities of
pilots interacting with highly automated flight deck [7][17].
In order to better understand why trained pilots fail Eye-tracker and Areas of Interest
sometimes to adequately monitor flight parameters, the Eye tracking data were collected with two synchronized
DGAC/DSAC initiated the Pilot Vision project. This Pertech eye-trackers (0.25° – 0.5° of accuracy). Head
project aimed at analyzing eye tracking data collected by movements were corrected by an alignment of three infra-
ISAE in different full flight simulators (Airbus-A330 and red emitters to map participants’ fixations on an image of
Boeing-777 full flight simulators) during approach phase reference (see Figure 1 for a graphical view). The 15
preceding a go-around procedure. following AOIs were created (see Figure 2): 1) Airspeed
(Speed), 2) Attitude indicator (AI), 3) Altitude indicator
In this paper, a first analysis conducted over 32 approaches
(Alt.), 4) Heading (HDG), 5) Flight Mode Annunciator
is detailed. The aim of this study was to address the
(FMA), 6) Navigation Display (ND), 7) Electronic
following questions: 1) In which proportion do the PF and
Centralized Aircraft Monitor (ECAM) for Airbus-A330 and
the PM glances at the different areas of interests (AOIs) of
Engine Indicating and Crew Alerting System (EICAS) for
the cockpit flight deck during the approach? 2) Does the
Boeing-777, 8) left Multipurpose Control Display Unit
monitoring in line with the SOPs? 3) Does the pilots’ status
(MCDU), 9) right MCDU, 10) External view (Ext.), 11)
(Captain versus First Officer) interfere with the pilots’ role
Auto Flight Control Panel: Flight Control Unit (FCU) for
in the cockpit (PM versus PF)?
Airbus-330 and Multi Control Panel (MCP) for Boeing-
777, 12) Flaps control panel (Flaps), 13) landing gears
control panel (Gears), 14 ) No Zone (NZ; i.e. all what is
being viewed and which does not correspond to an AOI),
and 15) Out of Zone (OZ; i.e. including all the data that
was not captured by the device; this is not an AOI but a
non-captured quantity of data).
Figure 2 — Example of Captain’s AOIs delimiting flight deck
instruments (Airbus A330).
Average percentage of dwell time on each AOI was
calculated for each participant, for each approach.
Figure 1 — Heatmaps examples of a Captain when he is PM (on top) and
a First Officer when he is PF (at the bottom), between 1500 and 500 ft (the
two pictures come from an Airbus A330 cockpit). The ‘hot’ zones where Procedure
the users focused their gaze with a higher frequency are indicated by Data were collected during the ASAGA project (Aeroplane
yellow/red colors whereas less attended zones are depicted in dark/blue State Awareness during Go-Around) leaded by the Bureau
colors.
d’Enquêtes et d’Analyses (BEA), the French safety board.
This project was dedicated to the study of PM and PF’s eye
MATERIAL AND METHOD
movements during go-around procedures (GA) in Boeing
Participants
777 and Airbus A330 full flight simulators, equitably
Eight voluntary French crews (i.e., one Captain and one distributed over the crews (please note that these two
First Officer) coming from different airlines, 16 pilots in aircraft simulators were not equipped with a head-up
total, took part to the experiment. The roles of the Captains display) [1]. During the experiment, the crews executed
and the First Officers (i.e., PM and PF) varied during the four different approach procedures: the first three leading to
flight (see next section). PFs’ mean age was 46.5 years (SD a GA during the short final and the last one leading to a
= 7.7) with a mean flight experience of 14450 hours (SD = landing. We unfortunately did not counterbalance the order
2192). PMs were on average 41.3 years (SD = 4.3), with a of the landings across the participants due to
mean flight experience of 7991 hours (SD = 2795). implementation constraints in the full flight simulators. In
the present paper, we focused our analyses on the four approach, with tailwind will increase from 15 to 20 kt.
approaches. Each approach was segregated into three ATC effectively announced this wind conditions change
phases: 2500 – 1500 ft, 1500 – 500 ft and 500 ft – TOGA only during short final approach. The crew was expected to
or touch down (this last phase was temporally delimited perform a GA.
when the PF put his hands and the thrust levers in order to Approach 3) GA due to IMC (Captain = PM, First Officer
execute the GA). In the two first scenarios, the Captain was = PF): Still above Marseille airport, the crew had to make a
PF and the First Officer was PM. In the last two scenarios, breakthrough LOC DME 13 left under radar guidance.
the Captain was PM and the First Officer was PF. However, poor visibility led the crew to execute a GA.
Approach 1) GA requested from ATC (Captain = PF, First Approach 4) and landing (Captain = PM, First Officer =
Officer = PM): While the crew began an approach on PF): After the third GA, the crew managed to land on
runway 18 at Lyon airport, ATC announced a change in the Marseille Airport.
selected runway then requested an Instrument Landing
System (ILS) break in 36 left under radar guidance to
occupy the crew during approach. Under 200 ft, runway RESULTS
was occupied and ATC requested for an unexpected GA. Descriptive analysis
This first GA was ordered by ATC and disrupted by a The next figures illustrate our descriptive results. We first
change in the aimed altitude. merged AOIs related to the primary and secondary
Approach 2) GA due to tailwind conditions (Captain = PF, instruments for both PF and PM (see Figure 3). Then, the
First Officer = PM): The crew had to change aircraft average percentage of dwell time on the different AOIs in
course to Marseille airport in accordance with the flight two different ways was plotted to highlight the possible
record. They had to execute a standard ILS 31 right Z interactions between pilot’s status and role (see Figure 4).
Figure 3 — Average dwell time for PF (on the left) and PM (on the right), regarding Primary instruments (i.e. Airspeed, Heading, Attitude and Altitude
indicators), Secondary instruments (i.e. FMA, ND, ECAM/ECAIS, FCU/MCP, MCDUs, Flaps panel, and landing Gears), External view, Out of Zone (OZ)
and No Zone (NZ).
Figure 4 — Average dwell time per AOI, per role then per status (on the left), and per status then per role (on the right), for all the four approaches.
Eventually, the last figure illustrates PF and PM average percentage of dwell time during the three segments of the approach.
Figure 5 — Average dwell time per AOI, per phases and roles, over all approaches.
Statistical analyses Tukey’s HSD post hoc analysis for Role × Grouped AOIs
Primary vs. secondary instruments interaction showed that PF (M = 51.59, SD = 12.11) fixed
Approaches 1 and 2 were averaged into a first single more at primary instruments than PM (M = 35.74, SD =
situation in which Captain was PF and First Officer was 7.09, p < .001). No other significant result was found.
PM, and approaches 3 and 4 were averaged into a second Considering all AOIs separately
single situation in which Captain was PM and First Officer A second general mixed 14 × 2 × 2 (AOIs [Speed, Attitude
was PF. indicator, Altitude indicator, Heading, FMA, ND,
AOIs were first merged into two main groups, which are ECAM/ECAIS, FCU/MCP, left MCDU, right MCDU,
Primary instruments and Secondary instruments (see Figure Flaps, External view, NZ, OZ] × Role [PF, PM] × Status
3). These two groups were taken as grouped AOIs then [Captain, First Officer]) ANOVA with both AOI and Role
compared with External view, NZ and OZ. implemented as within factors and Status implemented as
A first inferential analysis was performed using Statistica between factor, was performed (see Table 2 for the results).
10. A general mixed 5 × 2 × 2 (Grouped AOIs [Primary Data collected for Gears AOI being not balanced because
instruments, Secondary instruments, Ext., OZ, NZ] × Role they were missing for one session, the study does not
[PF, PM] × Status [Captain, First Officer]) analysis of integrate them into the statistical analyses.
variance (ANOVA) with both Grouped AOIs and Role This ANOVA showed a main effect of the role [F(1, 13) =
implemented as within factors and Status implemented as 46.24, p < .001, η²p = .780]. The analysis revealed a second
between factor. main effect of the AOIs, [F(13, 169) = 66.27, p < .001, η²p
This first ANOVA revealed a main effect of the AOIs, = .84] and a Role × AOIs interaction [F(13, 169) = 15.23, p
[F(4, 56) = 115.00, p < .001, η²p = .89], and a Role × < .001, η²p = .54].
Grouped AOIs interaction [F(4, 56) = 17.00, p < .001, η²p =
.55] (see next Table 1 for a summary of the results). General ANOVA
Variable ddl F p η²p
AOIs 13, 169 66.27 < .001* .84
General ANOVA (with grouped AOIs) Role 1, 13 46.24 < .001* .78
Variable ddl F p η²p Status 1, 13 .14 .72 .01
Grouped AOIs 4, 56 115.00 < .001* .89 Role × Status 1, 13 .36 .56 .03
Role 1, 14 1.00 .29 .08 Role × AOIs 13, 169 15.23 < .001* .54
Status 1, 14 1.00 .44 .04 Status × AOIs 13, 169 1.75 .06 .12
Role × Status 1, 14 1.00 .30 .08 Role × AOIs × Status 13, 169 1.52 .12 .11
Role × gr. AOIs 4, 56 17.00 < .001* .55
Table 2 — Results from 14 AOIs × 2 Roles × 2 Status general ANOVA.
Status × gr. AOIs 4, 56 1.00 .43 .07
Role × gr. AOIs × Status 4, 56 1.00 .60 .05 To simplify statistical analyses, twelve different mixed
Table 1 — Results from 5 AOIs (Primary instruments, Secondary ANOVAs were launched, one per AOI (i.e. Airspeed,
instruments, Ext., OZ and NZ) × 2 Roles × 2 Status ANOVA.
Attitude and Altitude indicators, Heading, FMA, ND, 0.1, η²p = .39]. Post hoc analysis revealed that First Officers
ECAM/ECAIS, FCU, MCDUs, Flaps panel and External glanced more at it when they were PM (M = 3.04, SD =
view). 5.30) than when they were PF (M = .53, SD = 1.16, p <
For each AIO, a mixed 2 × 2 (Role [PF, PM] × Status .05). Moreover, First Officers glanced more at right MCDU
[Captain, First Officer]) ANOVA was run with Role (M = 3.04, SD = 5.30) than Captains (M = .16, SD = .34, p
implemented as within factor and Status implemented as < .01) where they were both PM.
between factor. Tukey honest significant difference
(Tukey’s HSD) was used for post hoc testing. No statistical result was found for the following
Airspeed. The analysis revealed a main effect of status instruments: FMA, ND, FCU/MCP, Left MCDU, Flaps
[F(1, 14) = 5.60, p < .05, η²p = .29], with Captains (M = panel and External view.
5.96, SD = 1.42) glancing significantly less at the Airspeed
indicator than First Officers (M = 8.38, SD = 2.53). A
trend was found for the role, [F(1, 14) = 4.03, p = .06, η²p To summarize, these statistical analyses revealed that:
= .22], with PF (M = 7.79, SD = 6.17) having high values (i) PF fixed more the Primary instruments (Attitude and
for this AOI than PM (M = 6.55, SD = 4.96). The analysis Altitude indicators, Speed and Heading) than PM (see
showed a significant Role × Status interaction [F(1, 14) = Table 1);
6.14, p < .05, η²p = .31]. Post hoc analyses revealed that (ii) PF and PM exhibited almost similar average
Captains gazed more at the airspeed when they were PF (M percentage of dwell time on the Airspeed, Altitude
= 7.35, SD = 6.24) than when they were PM (M = 4.57, SD indicator, Heading (i.e. three out of four primary
= 3.07, p < .05). When endorsing the role of PM, First instruments), FMA, ND, FCU/MCP, left MCDU,
Officer glanced significantly more at the Airspeed indicator Flaps panel (the most part of secondary instruments),
(M = 8.53, SD = 5.68) than Captains (M = 4.57, SD = 3.07, and moreover External view AOIs;
p < .05). Finally, no difference on this AOI where found
when First Officers acted as PF or PM. (iii) The PF glanced more at the Attitude Indicator than the
PM;
Attitude indicator. The statistical analysis revealed a main
effect of role [F(1, 14) = 31.42, p < .001, η²p = .69], with (iv) The status of the pilot led them to glance differently at
PFs spending more time staring at the attitude indicator (M primary instruments Airspeed (more fixed by First
= 30.96, SD = 18.57) than PMs (M = 16.19, SD = 8.14). No Officers) and Heading (more fixed by Captains);
effect of the status, nor Role × Status interaction were (v) There is also a Role × Status interaction on Airspeed:
found. with PM–First Officers glancing more at the Airspeed
Altitude indicator. The analysis revealed a significant than PM–Captains.
Role × Status interaction [F(1, 14) = 11.61, p < .01, η²p =
.45], but the post hoc analysis revealed no significant DISCUSSION
differences. No PM/PF difference and no effect of the The objective of the present paper was to present the
status were found. preliminary findings conducted over eight crews
Heading. The analysis revealed a significant main effect of performing four stabilized approaches (i.e. 32 analyses).
status [F(1, 14) = 10.33, p < .01, η²p = .43], with Captains The main motivation was to investigate PM and PF’s
glancing more (M = 5.19, SD = 2.92) at the Heading ocular behavior (in terms of dwell times) with regard to
indicator than First Officers (M = 1.53, SD = 1.37). The SOPs. To the authors’ knowledge, this study was the first to
analysis revealed no significant effect between PM and PF, measure both PM and PF’s eye movements in the context
or Role × Status interaction. of realistic operational scenario, with multi-crew
ECAM/ECAIS. The statistical analysis for this AOI environment in full flight simulator and during approach
revealed a significant main effect of role [F(1, 14) = 14.55, phases.
p < 0.1, η²p = .51] with PFs (M = 2.18, SD = 2.98) glancing Firstly, the results confirmed that both pilots glanced more
less at the ECAM/ECAIS than PMs (M = 3.92, SD = 4.03, at the primary flight parameters than the secondary ones.
p < .01). No effect of status nor Role × Status interaction This is consistent with the SOPs as it is mandatory for
were found. pilots to particularly monitor the parameters such as the
Right MCDU. The statistical analysis revealed a main aircraft attitude, the speed, the altitude and the heading.
effect of role [F(1, 14) = 5.04, p < 0.5, η²p = .27], with PM We also note that the rank ordering of the different AOIs is
(M = 1.60, SD = 4.01) spending more time on it than PF (M closely correlated with the scan data of PF observed by
= .53, SD = 1.36, p < .05). A significant main effect of [17], for commercial PF and for pilots [7].
status was found [F(1, 14) = 5.80, p < 0.5, η²p = .29], with Secondly, the results showed that the PF and the PM
Captains (M = .34, SD = 1.13) glancing less at it than First exhibited the same ocular behavior at the exception of the
Officers (M = 1.79, SD = 4.02, p < .05). A significant Role Attitude indicator. Indeed, the PF glanced more at the
× Status interaction was also found [F(1, 14) = 8.99, p < Attitude indicator than the PM. Though this latter result is
consistent with the SOPs (i.e. the PF is in charge of include specific training pertaining to improve
monitoring the trajectory), one could have expected more monitoring”.
pronounced differences between the PF and the PM’s However, this study has several limitations that need to be
ocular behavior. Indeed, these two pilots are supposed to considered. First, the eye tracking results have to be taken
behave differently as defined by the SOPs. We found no with care. Indeed, our sample was composed of only 16
effect of the status on the pilots’ behavior to the exception pilots (eight crews) and the accuracy of eye-tracking
of the speed indicator. That is, whatever their status and techniques still remains a challenge, especially in
experience (i.e. Captains are more experienced than First ecological conditions. Secondly we did not counterbalance
Officers), all PMs and PFs behaved the same way. Only the properly the order and the design (i.e. PF, PM) of the four
Captains, when they were PM, glanced more at the airspeed approaches. This design was imposed by the ASAGA
indicator than when they were PF. However, Captains project and the complexity of using full flight simulators.
fixated less this critical AOI than First Officers. Thirdly, this study was conducted with French pilots and
Thirdly, results suggested that PMs’ visual behavior was thus it does not take into account cultural effect. Actually,
not optimal regarding the prioritization of the flight it’s known that several differences exist between countries
parameters. For instance, PMs spent few time monitoring concerning the conduct of checklists and procedures [12].
the speed during the three sequences of the approach. PMs Eventually, our study will continue with the analyses of the
progressively redistributed their attention across the three remaining data collected during the ASAGA project (i.e.
segments to particularly focus on the external view. This four more crews – 16 approaches), and other data collected
was particularly true during the last sequence (500 ft to during unstabilized approach (4 landings × 28 PFs) in
GA/touch down) when PMs spend approximately 35% of Boeing-737 full flight simulator.
the time on the external view. This finding is surprising as
long as PMs are supposed to keep their head down to Focus should be placed on the analysis of pilots’ ocular
monitor critical flight parameters related to the aircraft dynamics via their scan path, and other eye metrics such as
state. This possible misallocation of attention could find saccades, number of fixations, or time duration between
two explanations. The first one is that the pilots can two fixations, for example [8].
monitor the speed using peripheral vision/covert attention
thanks to the “speed trend vector” [7] (i.e. an arrow ACKNOWLEDGMENTS
indicating the speed value in the next 10 seconds). This We would like to thank Guillaume Adam (BEA), leader of
indicator can lead to less eye movements to integrate the ASAGA project, and all the pilots who took part in this
“trends” of the speed rather than the exact value of the experiment.
speed itself. A second explanation may rely on automation
bias issues [16] leading the pilots to over rely on the auto-
thrust to manage the speed, and thus to pay less attention to REFERENCES
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