stanton 1997 safety-science

8/4/2019 Stanton 1997 Safety-Science

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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.

149



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



Drive-by-w ire: the cas eof driver w orkload and reclaimin g control w ith adapt ice cruise control 151

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



152 N.A. S tunton et al.

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



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).



154 N.A. Stanton et al.

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.



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.



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-



Drive-by-w ire: t he cas eof driver w orkload and reclaimin g control w ith adapt ive cruise control 1.57

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



158 N.A. Stanton et al.

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



Drive-by-w ire: the cas eof drioer w orkload and reclaimin g control w ith adapt ive cruise control 159

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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