stanton 1997 safety-science
TRANSCRIPT
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Pergamon
Safety Science Vol. 27, NO. 2/3. pp. 149- 159. 1997
0 1997 Elsevier Science Ltd. All rights reserved
Printed in Great Britain
092%7535/97 $I 7.00 + 0.00
PII: SO9257535(97MOO54-4
DRIVE-BY-WIRE: THE CASE OF DRIVER
WORKLOAD AND RECLAIMING CONTROL
WITH ADAPTIVE CRUISE CONTROL
N.A. Stanton *, M. Young, B. McCaulder
Department of Psychology, University of Southampton, Highfield, Southampton SO1 7l&i. UK
Abstract-Vehicle automation is highly likely to be in service by the end of this century. Whilst
there are undoubtedly some benefits associated with such systems, there are some concerns
also. This paper presents work in progress on the Southampton Driver Simulator on driver
workload and the driver’s ability to reclaim control from the Adaptive Cruise Control system in
a malignant scenario. Previous studies suggest that there may be some cause for concern, This
study shows a reduction in mental workload, within a secondary task paradigm, associated with
operating Adaptive Cruise Control. This finding is contrary to previous research into Adaptive
Cruise Control. Further, in line with other research, this study shows that a third of the
participants were unsuccessful in reclaiming control of the vehicle before a collision occurred.We suggest that more research and development effort needs to be spent on looking at the
communication between Adaptive Cruise Control and the driver. 0 1997 Elsevier Science Ltd.
All rights reserved.
I&words: Automation: Workload; Driving; Adaptive cruise control; Collisions; Human factors
1. Introduction
This paper develops the theme of vehicle automation from a previous paper published in
this journal by Stanton and Mar sden (199 6) which considers the safety implications of
drive-by-wire systems . Stanton and Ma rsden (19 96) wer e concerned with identifying the
lessons learnt in the aviation environment with fly-by-wire systems, in particular they cite
problems assoc iated with shortfalls in expe cted benefits, equipme nt reliability, training and
skills maintenance, and error inducing equipment designs. For a more detailed review of the
general issues in vehicle automation the reade r is referr ed to Stanton and Mar sden (199 6).
This paper will consider some of these issues with respect to Adaptive Cruise C ontrol which is
part of an ongoing research p rogramme within the Southampton Driver Simulator. There is
* Corresponding author. Tel.: t44 170 359 2586; Fax: +44 170 359 4597; e-mail: nas@so-tonacuk.
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15 0 N.A. Stanton tal.
unfortunately little resea rch in this area repor ted in academ ic journals, a situation we seek to
rectify. T he study reported in this pape r represents the start of our investigations. We
apprec iate that there is still a long journey ahea d of us, but we feel that our investigations thus
far present us with some interesting findings we would like to share w ith our peers .
Adaptive Cruise Control (A CC) hera lds a new generation in vehicle automation. AC Ccontrols both speed and headway of the vehicle, slowing the vehicle down w hen presented
with an obstacle and restoring target speed when the obstacle is removed. In this way ACC
differs from traditional Cruise Control (CC ) systems . In traditional cruise control, the system
relieves the driver o f foot control of the acceler ator only (i.e. relieving the driver of some
physical workload), whereas ACC relieves the driver of some of the decision making elements
of the task, such as deciding to brake or change lanes (i.e. relieving the driver of some m ental
work load), as well as physical deman ds of accelera tor control. Potentially, then, AC C is a
welcom e additional vehicle sy stem tha t will add com fort and convenience to the driver
(Nilsson, 1995). Typical driving patterns in terms of speed and headway suggest that a more
constant speed and following behaviour is produced when the ACC system is engaged (Faber,
1996). This change in driving pattern produced by ACC is expected to ease traffic flow
leading to greate r throughpu t and a reduction in both congestion and accidents, primarily rear
end shunts arising throug h either lack of respons e to harsh braking by vehicles ahead o r by
mis-judgement of the speed of approach towards a slow moving vehicle ahead. These
represent about 15% of fatal accidents on motorways (Faber, 1996). Studies in the UK
suggests that between 5% and 10% of these motorway accidents could be avoided with the
help of ACC (Broughton and Markey, 1996).
It is anticipated that by the end of this century AC C systems will be standard on luxury cars
and optional on other vehicles. Within ten years w e are likely to see AC C becom e a commo n
feature in vehicles, as indeed automa tic transmission and pow er assisted steering hav e
becom e. Due to the potential safety critical nature of assigning control of the vehicle totechnology, leaving the driver in the role of monitoring the system, it is essential that resea rch
is conducted into the ability of the driver to reclaim control as well as investigations into the
driver’s understanding of the automa ted system. T he idea of automation of driver functions as
a panacea to problems related to driving is being constantly reinforced. Stanton and Mar sden
(199 6) identify a number of arguments in favour of automation, for exam ple, it can improve
the driver’s well-being, it can improve road safety and it can enhance produ ct sales. They also
sugge st that automation may have an effect upon the demand made upon drivers’ limited pools
of attentional resources by relieving them of mental w orkload. Compare the cases where
conventional Cruise Control (CC ) is engage d and where it is not. To set up CC the driver
reaches the speed they wish to cruise at through manual operation and then press the CC
button. In operation the CC functions rather like a therm ostat in a heating system. If the speed
of the car is below a set target then the accelerator is applied to bring speed in line w ith the
target. W ith C C engaged the driver is apparently free of the task of holding his foot on the
accelerator pedal. By removing this task the driver has a new task - one of preparing to
intervene if the vehicle e ncroac hes on another (a monitoring task). Should the car becom e too
close to the one in front the driver has to disengage CC and take control again or change lanes.
We feel that CC represents a half-way house between manual operation and full automation.
The driver is still in the control loop to some e xtent, but has to make a conscious decision to
assume control by disengaging the CC system. Without CC engaged the driver is not troubled
with these changes in activity and perfor ms the driving tasks tacitly. Per haps one of the
reasons for the limited success of CC in the UK was the frequency with which CC had to be
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disengaged to cope with driving on our overcrowded highways. Microprocessor technology
offers a technological solution to this problem: automation. AC C is an engineering improve-
ment on CC, a radar mounted at the front of the car can detect vehicles in the path of the car
and can brake automatically. When the radar indicates that there is no longer a vehicle in its
path it signals that the acceler ator may be applied to return the vehicle to its previous setspeed . Thus the driver is relieved of braking and accelerating tasks.
However, Stanton and Marsden (1996) also caution that automated systems are not without
their problems. Bas ed upon an evaluation of automation in aviation, which the y take to be
developmen t ground for the concep ts that are now entering into landbased transportation, they
sugges t that autom ated systems are frequently less reliable than anticipated when they are
introduced into the operational arena. There are three main concerns. First, that drivers w ill
becom e over-reliant upon the automa ted systems . Second, th at drivers will evoke the systems
in situation beyond their original design pa rame ters. Third, th at drivers will fail to appre ciate
that the system is behaving in a way that is contrary to their expectation s.
One of the biggest unknowns in AC C opera tion is me reaction of the driver to the apparen t
loss of some of their driving autonomy. Because the ACC system will not cater for every
potential traffic scenario, it is essential that the driver has a clear understanding of the system
operation, and also the points at which they will need to intervene in the automatic operation
of the vehicle. It is envisaged that although the ACC system will behave in exactly the manner
prescribed by the designers and programmers, this may lead to some scenarios in which the
driver’s perception of the situation is at odds with the system operation (Stanton and Mar sden,
1996 ). Thes e scenarios may be coarsely classified into situations whe re the object detection
mechanism may not detect targ ets in the path of the vehicle (e.g. motorcycles) and situations
where the object detection mechanism picks up false targets (such as crash barriers). These
situations may occur in contexts which hav e benign (e.g. situations that lead to deceleration
with no vehicles following) and potentially malignant consequence s (e.g. situations that leadto the vehicle accelerating into another vehicle in its path). The se kinds o f situations raise the
question of the driver’s ability to reclaim control in an effective and safe manner.
A previous study suggests that ACC will be readily accepted by drivers. Nilsson (1995)
com pared drivers’ behaviour in critical situations with and without the assistance of AC C in a
simulated driving environment. The three scenarios und er investigation were approac hing a
stationary queue of traffic, a car pulling out in front of the participants’ vehicle and hard
braking by the lead vehicle. All of these scenar ios required intervention by the participant.
Nilsson found that only in the first scenario did drivers with ACC fail to intervene in a timely
manner. Nilsson sugges ts that this is likely to be due to the expectation of the drivers that the
AC C system wou ld cope with the situation effectively. Interestingly, Nilsson found no
statistical differences in the level of mental work load between the AC C and manual condi-
tions.
Simulator studies have several advantages for research of this nature (Senders, 199 1). First,
they can be used to put people into situations which would not be ethical in the real
environment, such as life threatening situations. Second , simulators can be used in carefully
controlled experimental studies, so that we may be sure that it is the experimental variables
being manipulated that result in differences in driver performanc e, not other confounding
variables. Finally, we are able to comp ress experience , to collect data on a whole range of
situations unlikely to be encountered in the natural environment in a short time frame. T he use
of simulation in resea rch environments is not without controversy. In a recent review of the
literature Stanton (199 6) identified the main issues surrounding simulator use wer e focuse d
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upon the level of fidelity encapsulated within the simulated environment. Thes e issues are
apparently domain independent and certainly apply to driving simulators. Two major issues
can be identified as physical (i.e. the degr ee to which the simulated environment looks like the
real environment) and functional (i.e. the degr ee to which the simulated environment behaves
like the real environment) fidelity. Simulators appe ar to have been used with some succe ss inresea rch on driving (e.g . Michon, 1993; Nilsson, 199.5; Bloomf ield and Carroll, 1996 ). The
resea rch evidence seem s to sugges t that functional fidelity is of greate st im portance to transfer
effects , i.e. the degr ee to which behaviour in the simulator transfers to the real operational
environment (Senders , 1991; Stanton, 1996 ). Physical fidelity ma y help convince the experi-
mental participant that the task should b e taken seriously which w ould be less convincing in a
more abstract environment.
The study in this wor k will examine the ability of drivers to reclaim control under a
malignant failure scenario, whe re the AC C system fails to detect a vehicle in its path, and
com pare the level of mental work load with manual control of the vehicle. On the basis of
Nilsson’s (1995) work, w e expected to drivers to have some difficulty in detecting the system
failure. The literature on workload is more equivocal, so we decided to employ a secondary
task paradigm to find out if drivers had more spare attentional capacity when the ACC system
was evoked.
2. Method
2. I. Participants
Twelve drivers (six male and six female) with a mean age of 21 years pa rticipated in this
study. The participants wer e undergra duates at the University of Southam pton and held fullBritish driving licences for an averag e of 3.4 years. All participants were treated according to
the British Psycholog ical Society’s ru les governing ethical protoco l in psychological resea rch.
2.2. Equipment
The Southampton Driver Simulator was used as the experimental environment. The
simulator c omprise s an Archim edes RISC comp uter running simulation softw are, an Epson
colour pro jection monitor, a projection screen an d the front portion of a Ford Orion fitted with
transducer s that commu nicate the drivers action to the simulator softw are which alters the
viewed image accordingly. The layout of the simulator set-up is shown in Fig. 1. The
simulation is fully interactive: the driver h as full vehicle control and may interact with other
vehicles on the road. The data logged include: spee d, position on the road, distance from other
vehicles, steering whee l and pedal positions, overtak es and collisions.
2.3. Experimental design
A completely repea ted factorial design was employe d in the study to ensure that all
participants experience d all experimental and control conditions. Me asures wer e collected of
all primary driving tas k perform ance data (taken every 0.5 seconds automatically by the
simulator software), secondary task (using the rotated figures task, Baber, 1991) data were
collected to provide a measu re of work load. As is shown in Fig. 2, the secondary task stimuli
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Drioe-by -w ire: the cas eo f driver w orkload and reclaimin g control w ith adapt ive cruise control 153
Fig. 1. The Southampton Driver Simulator set-up.
wer e presented at the bottom left-hand corner of the display. This was within the same visual
field as the road view. The aim of the secondary task was to quantify spare attentional
capacity (Stok es e t al., 1990; Wickens, 1992; Schlegel, 19931, therefore participants were
explicitly instructed only to respond to the secondar y task when the demand from the primary
task (i.e. driving the car safely) permitted. Responses to the rotated figures were recorded by
presses on the control storks attached to the steering column. ‘Same’ judgements were
recorded by presses to the left control stork and ‘different’ judgements were recorded bypress es to the right control stork. Wh ilst it is accep ted that attending and responding to the
secondary task will occupy the same attentional and physical resour ces as driving ( i.e. looking
at the rotated figure occupies visual attention and responding to the rotated figures occupies
Fig. 2. The driver’s view of the road, instruments and secondary task (see bottom left of picture).
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manual responses) we suggest that these are measures of spare capacity rather than intruding
upon the primary task.
The study was devised to investigate the work load deman ded by the driving task in manual
and autom ated scenarios. In addition, the autom ated condition was designed to present a
failure situation that is anticipated in AC C operation. The most malignant failure scenario isunexpe cted accelerating by the AC C system when the re is a vehicle in its path. This could
occur due to a technical malfunction and would req uire the driver to reclaim manual control of
the vehicle.
2.4. Procedure
Participants were briefed that the study was about vehicle automation and were shown the
simulator. It was explained to them that they were free to withdraw at any time. Upon
agreeing to participate, they sat in the car and adjusted the seat to suit their preferences. Then
they we re asked to drive the car in order to acclimatise to the controls and feel of the
simulator. The participants were also asked to practice the secondary task. This process takes
five minutes for most participants.
The experimental session was separ ated into three trials. In the first trial participants wer e
aske d to drive the car manually along th e road. They wer e instructed to follow a vehicle at a
comfortable distance for the duration of the trial. They were also asked to attend to the
secondar y task whenev er they could. In the second trial, participants wer e aske d to drive up to
the lead vehicle a s before, but once behind it they should en gage the ACC system and follow
the car for the rest of the trial with ACC engaged. Again they were instructed to attend to the
secondar y task whene ver they could. In the final trial, participants were instructed exactly a s
they were in the second trial. This trial involved deception of the participant, as the AC C
system was designed to fail some time after it had been engaged by accelerating theparticipant into the lead vehicle. If the participant took no, or inappropriate, evasive action
then the vehicle would ‘crash ’ into the lead vehicle.
After completing the trials, participants were debriefed on the nature of the study and asked
for their biographical details. Total time in the experimental session w as approxim ately 30
minutes.
2.5. Analysis
The data for participants were organised into 12 blocks for the repeated measures design.
As data for each participant were recorded every 0.5 second, blocks were used as a convenient
means of averaging the data over time. Analysis of Variance (ANO VA) was conducted on the
data derived from the simulator, comprising: position on the vehicle on the road, distance from
the lead vehicle, spee d of the vehicle, acceler ator input, brake input and distance from the lead
vehicle. The secondary task data wer e analysed using Wilcoxon signed-ranks test.
3. Results
The results section is divided into three parts. The first part deals with the analysis of data
from the driving simulator. The second part deals with the analysis of work load. The third part
deals with the driver’s ability to reclaim control when the AC C system failed.
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Driw by-w ire: the cas rof driver w orkload and reclaim ing cont rol w ith adapt ice cruise control 155
0
Manual ACC
Experimental conditions
Fig. 3. Correct responses to the secondary task in the manual and ACC conditions.
3. I. Southam pton Dricer Simulat or
The data about th e comparing the position of the vehicle on the road (F,,,, = 0.001 ,
p = NS), distance from the lead vehicle (F,,22 = 0.005, p = NS) and speed of the vehicle
(F,,22 = 0.456, p = NS) for the manual and automa ted conditions were non-significant. This
means tha t there were no statistically significant differences in driver behaviour in the
autom ated and manual condition for these three variables. It is interesting to note that therewas no statistical difference in the distance drivers kept from the lead vehicle in both
conditions.
??Crash
H steer
I4 Steer + brake
Fig. 4. Driver reactions to ACC failure.
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156 N.A. Stanto n et al.
There were significant differences in the acceler ator ( F,.,2 = 159.5 19, p < 0.0001) and
brake (F, ,2z = 86.08 7, p < 0.000 1) inputs between the manual and autom ated conditions. This
is, however, an artefact of the way in which the ACC system was designed to work in the
simulator.
3.2. Analysis of workload
The secondary task show ed significant differences between the manual and automa ted
conditions (Z corrected for ties = - 4.267, p < 0.000 1) with significantly more items being
correctly identified by participants in the automa ted condition, as illustrated in Fig. 3.
As Fig. 3 shows, the workload demands were greater in the manual condition, as
participants had less free time to tackle the rotated figures task.
3.3. Reclaiming control
As Fig. 4 show s, four of the twelve participants failed to reclaim control of the vehicle in
an effective manner before it crash ed into the lead vehicle. How ever, eight of the participants
did respond effectively. Two participants steered o ut of trouble and six participants employed
the strategies of steering and braking togethe r.
4. Discussion
These new systems appear to have certain resident pathogens (Norman, 1990), not least of
which includes the effects upon mental work load and problems related to restricted operation.
Automation may remov e some tasks, such as braking and accelerating, but at the same time itadds new tasks. In the case of ACC the driver now has to monitor the ACC system to make
sure that it is working properly. This monitoring task provides the driver w ith the problem of
determining when the system has failed. It could fail in four main ways: braking when it
should not, accelerating when it should not, failing to brake and failing to accelera te. The
failure sc enarios of most concern are the failure to brake and the unjustified acceleration, as
the other scenarios are unlikely to put the driver in immediate danger. The scenarios that give
the most cause for concern may also be the harde r to distinguish. As the vehicle encroac hes on
the vehicle in front the driver will need to reach a judgement about the need fo r intervention
and success will be highly time dependent. The irony is that by automating the task the driver
may become underloaded and thus reduce the level of attention devoted to the task as the
driver is remo ved from the control loop. Paradoxically, the driver may also be overload ed in
emergency situations. Norman (1990) suggested that the major problem with automated
systems is that by removing the human opera tor from the control loop they are also likely to
prevent them from detecting symptom s of trouble in time to do anything about them. In
studies on unintended acceleration (Schm idt, 1993 ) some parallels to the shortcom ings of
automation on drivers’ behaviour may be drawn. Admittedly the unintended acceleration
literature is concerned with driving off with automatic transmission and disengaging CC when
leaving a freeway, but we suspect that the latter scenario may be quite close to ACC scenarios.
Several incidents h ave occur red wh ere vehicles have accelera ted uncontrollably when the
driver h as intended to disengage the cruise c ontrol by pressing the brake, but has inadvertently
pressed the accelerator. Schmidt pointed out that such events are rarely recovered immedi-
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ately, and the delay can range betwee n 8 and 40 seconds before the driver im plements an
effective strategy to avoid an accident. This sever e loss of control has been blamed upon a
panic phenomenon called hyper-vigilance. This reaction leads to perform ance decrem ents in
cognitive functioning. Behaviour al consequen ces of this decrem ent can include perseve rance
(wh ere the individual continues with the strategy), perceptu al narrowing (shutting out largeamounts of stimuli) and freezing (failing to take avoiding actions). In the case of unintended
acceleration, this means that drivers a ppea r to continue pressing the acceler ator, rather than
pressing the brake even when the car accelerates. Indeed, Schmidt (1993) observes that some
drivers press the accelerator (we suppose that they still expect the pedal to operate the braking
system) even more forcefully when the car does not slow down. This research evidence is
equivocal, how ever. Rog ers and Wierwille (198 8) repor t in investigations of acceler ators and
brake pedal actuation errors , in a simulated driving environment, experimental participants
immediately recognised accidental accelera tor activation. How ever, in their study, Rog ers and
Wierwille (1988 ) were simulating speed s around 2 0 mph in manual vehicle control (i.e.
non-automated tasks) whereas Schmidt (1993) was simulating motorway cruising speeds. One
explanation for the difference in the findings is the relatively rapid changes in acceleration that
occur w hen the vehicle is cruising at low speed versus th e relatively slow changes in
acceleration when the vehicle is cruising at motorw ay speed s. Another explanation considers
the differential effec ts that automation has upon driving. In manual control the error is noticed
immediately as the driver is within the control loop, where as in the autom ated scenario it takes
the driver a while to apprec iate that control is not being resum ed. We argue that this may be as
a consequence of being removed from the control loop.
Our study suggests that, like Nilsson’s (1995 ) scenario with a stationary queue, driver
intervention is less likely to be forthcoming when no overt changes occur in the external road
environment (i.e. other road vehicles show no change in their status). Nilsson’s study show ed
that when there was a drama tic change in external traffic hea dway , such as a vehicle pullingout in front of the driver or when the lead vehicle braked aggressively, then the driver with
ACC tends to reclaim control by braking. However, when there are no changes in the other
road vehicles we are reliant upon the driver appre ciating the significance of the closing gap
and no reduction in their own vehicle’s speed. The drivers seem to expect the ACC system to
intervene. but this trust may be misplaced on some occasions . Two thirds of the drivers in
Nilsson’s study and one third of the drivers in our study intervened too late to avoid a
collision. This leads us to suppo se that designers of AC C system s need to effectively
communicate the status of the ACC system to drivers to help them determine when
intervention is appro priate. In consideration of the coping strateg ies drivers employe d to avoid
collision it is perha ps worth y of note that two of the eight succ essful participants did not use
the brake, further indicating the effects of removing control from the driver. Only half of the
drivers reclaimed full control of the vehicle, using both the brake and steering systems to
avoid a collision with the lead vehicle.
We also note the differences in workload assessments by Nilssons’s study and our own.
Nilsson used the NASA-TL X (Hart and Staveland, 1988; Hendy e t al., 1993) which is a
respe cted subjective work load rating questionnaire that measu res six factors: mental demand ,
physical dema nd, time pressur e, perf orman ce, effort and frustration. Participants in Nilsson’s
study repor ted no statistically significant differences in the level of work load between the
manual and ACC conditions. However, our study shows, using the secondary task paradigm,
that the manual condition had a higher level of work load than the AC C condition. We feel that
the relationship between the secondary task paradigm and the NASA-T LX as workload
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measu res needs to be explore d further. We certainly believe that our results show , in an
objective manner, that work load is lower in the AC C condition, but we are concerned to clear
up this anomaly. The lower levels o f work load found by us in the AC C condition may indicate
the extent to which the driver was out of the vehicle control loop. W e feel that there is an
interesting relationship between the level of wor kload and the drivers ability to reclaim controlfrom the vehicle. In other areas of resea rch on human supervisory control it has been
suggested that reduced levels of attention associated with lower levels of workload may affect
the ability of the human opera tor to maintain an aware ness of the status of the system they are
monitoring (Woods, 1988; Sheriden, 1987). Woods (19881, in particular, discusses the
separation that occurs b etween wha t the human opera tor thinks the technical system is doing
and wha t the technical system is actually doing. T his separation, he argues , is one possible
cause for error s in system operation. In addition, this situation may be exace rbated by the
driver attending to other stimuli, such as the in-car audio sys tem or conversation with other
passen gers. Thes e postulations are rather speculative at the moment, but are ones we intend to
investigate further.
Finally our data, in contrast to Nilsson’s study, s how no difference in the gap between the
vehicles in the manual and automa ted conditions. Whilst th e design of AC C systems will
determine the size of the gap, normally determined by time for vehicle separation, it is an
interesting coincidence that the gap we designed into the simulator is the same that partici-
pants in the manual condition also chos e. Given the potential importance associa ted with the
ability of the driver to reclaim control, we feel that the issue of vehicle separation is likely to
be crucial. Obviously the bigger the gap the more time the driver ha s to intervene before a
collision becom es unavoidable. For manual vehicle control, the Departm ent of Transpo rt in
the UK recomm ends a minimum vehicle sep aration of two seconds. Arguably, given our
results, vehicle separation for AC C should be larger to provide drivers with the time reclaim
control for the automated system. The larger gap would provide the driver with more time toboth assess the situation and take evasive action. H owe ver, this needs to be contrasted with the
overall usability of the system and whether drivers and other road users would tolerate larger
vehicle separation. Thus it remains an empirical question which w e aim to answe r in future
studies.
5. Conclusions
Bainbridge (1983) pointed out the ironies of automation over a decade ago. She suggested
two main problems, first the assumptions designers make about the operators of the system,
second the tasks left over after automation. The first error leads to problems of operation of
the autom ated system (e.g. unintended acceleration). The second erro r leaves the driver coping
with the left-over task (e.g. typically monitoring the automatic systems ). We feel that more
effort needs to be expended on the driver interface of ACC systems to help drivers develop
appro priate internal mental representations that will enable them to understand the limitations,
and predict the behaviour of, AC C sys tems. We also feel that the link between the level of
attention of drivers and their ability to reclaim control needs to be explore d further. We intend
to continue this resea rch into the effec ts of vehicle automation on human perform ance. It is
only by measuring driver perfor mance with automation that we will be able to determine
whether the decrease in workload is detrimental. More research needs to be conducted
examining this link. Ideally, we should investigate differences in work load between those
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groups who succeed ed and failed in reclaiming control of the vehicle when the automation
failed. Unfortunately, in the current experiment, the size of the data set preclude s such an
analysis; howe ver this compar ison is planned in future s tudies. For now, we can be fairly
confident that automation at some level does reduce driver workload, and that performance is
degra ded in a critical situation when autom ation is engage d. We sugge st tha t the relationshipbetween these factors is a causative one; future res earch will determine whe ther this is the
case.
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