This article was downloaded by: [Stony Brook University]On: 24 October 2014, At: 13:06Publisher: Taylor & FrancisInforma Ltd Registered in England and Wales Registered Number: 1072954 Registeredoffice: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK

ErgonomicsPublication details, including instructions for authors andsubscription information:http://www.tandfonline.com/loi/terg20

Haptic gas pedal feedbackM. Mulder a , M. Mulder a , M. M. van Paassen a & D. A. Abbink ba Department of Aerospace Engineering , Delft University ofTechnology , Delft, The Netherlandsb Department of Mechanical Engineering , Delft University ofTechnology , Delft, The NetherlandsPublished online: 24 Oct 2008.

To cite this article: M. Mulder , M. Mulder , M. M. van Paassen & D. A. Abbink (2008) Haptic gaspedal feedback, Ergonomics, 51:11, 1710-1720, DOI: 10.1080/00140130802331583

To link to this article: http://dx.doi.org/10.1080/00140130802331583

PLEASE SCROLL DOWN FOR ARTICLE

Taylor & Francis makes every effort to ensure the accuracy of all the information (the“Content”) contained in the publications on our platform. However, Taylor & Francis,our agents, and our licensors make no representations or warranties whatsoever as tothe accuracy, completeness, or suitability for any purpose of the Content. Any opinionsand views expressed in this publication are the opinions and views of the authors,and are not the views of or endorsed by Taylor & Francis. The accuracy of the Contentshould not be relied upon and should be independently verified with primary sourcesof information. Taylor and Francis shall not be liable for any losses, actions, claims,proceedings, demands, costs, expenses, damages, and other liabilities whatsoever orhowsoever caused arising directly or indirectly in connection with, in relation to or arisingout of the use of the Content.

This article may be used for research, teaching, and private study purposes. Anysubstantial or systematic reproduction, redistribution, reselling, loan, sub-licensing,systematic supply, or distribution in any form to anyone is expressly forbidden. Terms &Conditions of access and use can be found at http://www.tandfonline.com/page/terms-and-conditions

Haptic gas pedal feedback

M. Muldera*, M. Muldera, M.M. Van Paassena and D.A. Abbinkb

aDepartment of Aerospace Engineering, Delft University of Technology, Delft, The Netherlands;bDepartment of Mechanical Engineering, Delft University of Technology, Delft, The Netherlands

Active driver support systems either automate a control task or present warnings todrivers when their safety is seriously degraded. In a novel approach, utilising neitherautomation nor discrete warnings, a haptic gas pedal (accelerator) interface wasdeveloped that continuously presents car-following support information, keeping thedriver in the loop. This interface was tested in a fixed-base driving simulator. Twenty-one drivers between the ages of 24 and 30 years participated in a driving experiment toinvestigate the effects of haptic gas pedal feedback on car-following behaviour. Resultsof the experiment indicate that when haptic feedback was presented to the drivers, someimprovement in car-following performance was achieved, while control activitydecreased. Further research is needed to investigate the effectiveness of the system inmore varied driving conditions. Haptics is an under-used modality in the application ofhuman support interfaces, which usually draw on vision or hearing. This studydemonstrates how haptics can be used to create an effective driver support interface.

Keywords: haptic feedback; car-following; driver support; human–machine systems;driving simulator

1. Introduction

Most of the active driver support systems available today can be labelled as what Goodrich

and Boer (2000) call task automation: safely promoting comfort. By automating routine

skills that were previously required of drivers, for example, speed regulation and car-

following with advanced cruise control (ACC), driving is made easier without compromis-

ing safety. More recent support systems are focusing on what Goodrich and Boer (2000)

call response automation: comfortably promoting safety. This type of automation informs

drivers when their safe-field-of-travel is degrading seriously, urging them to take

appropriate action to restore this situation to an acceptable level of safety. For example,

lane departure warning systems warn drivers that they are deviating dangerously from their

current lane, urging them to make corrective actions toward the lane centre.

The limited set of operational circumstances of task automation and the, usually,

discrete on/off nature of the currently available response automation systems pose prob-

lems for the effectiveness of these systems in enhancing safety. When operational con-

straints of task automation are not clear or are not easily identifiable, driver performance

might actually become worse in the most critical situations. Rudin-Brown and Parker

(2004), for example, found that, compared to unassisted driving, drivers using ACC took

*Corresponding author. Email: Mark.Mulder@TUDelft.NL

Ergonomics

Vol. 51, No. 11, November 2008, 1710–1720

ISSN 0014-0139 print/ISSN 1366-5847 online

� 2008 Taylor & Francis

DOI: 10.1080/00140130802331583

http://www.informaworld.com

Dow

nloa

ded

by [

Ston

y B

rook

Uni

vers

ity]

at 1

3:06

24

Oct

ober

201

4

longer to react to the lead vehicle’s brake lights, that is, when ACC’s braking authority

limits were reached and drivers had to take over longitudinal control of the vehicle.

A fundamental problem with discrete warning systems is the need to balance false

alarm rates and missed detections. Because of the severe consequences of a missed

detection, it is important that their number is near-zero. Dingus et al. (1997) show that, in

a simple calculation based on an estimated average driver involvement in a car crash of

once every five years and one out of five car crashes of a rear-end collision, drivers will,

theoretically, be involved in a rear-end crash only once every 25 years. This indicates that

true warnings rarely occur. To minimise the chance of a missed detection, however, the

frequency of false alarms and unnecessary alarms will be relatively high. Lees and Lee

(2007) point out that unnecessary alarms can foster driver trust and compliance to

subsequent warnings because they can help drivers understand how the warning system

works. False alarms fail in this respect, removing the possibilities for drivers to familiarise

themselves with the system. Without familiarisation, drivers might not be able to respond

as quickly and as appropriately to warnings as was intended in the design.

To overcome the potential negative human factors aspects that come with discrete

warning systems, while not constraining a support system to a limited set of operational

circumstances as with most task automation systems, a haptic gas-pedal interface was

investigated that displays car-following support information continuously. The motivation

for using haptic feedback was that it would allow drivers to virtually ‘touch’ their

environment through the haptic interface. Like obtaining visual information from the

driving scene by continuously scanning the road and other road users, the haptic

information would easily be available as an integral part of the control task of driving an

automobile.

Haptic gas-pedal feedback to support drivers in their driving tasks has been applied

before to speed control tasks, collision avoidance tasks and also car-following. Godthelp

and Schumann (1993) investigated an active gas pedal (accelerator) to support speed

control. The speed error relative to a normative speed limit was related to constant

counterforce on the gas pedal. They found that the haptic display yielded the lowest speed

error compared with alternative displays. Hjalmdahl et al. (2002) indicated that variance in

speed decreased and compliance with speed limits improved with their haptic gas pedal for

speed control. Verwey et al. (1993) also described a haptic gas-pedal collision avoidance

system that increased the force on the gas pedal with 25 N when – and for as long as – the

activation criterion was above a pre-determined threshold value. The haptic gas pedal with

a 4 s time-to-contact (TTC) activation criterion resulted in the largest decrease in

following separation and the least increase in driving speed and time spent in the left lane

(less overtaking). Continental Temic announced the launch of an Active Distance Support

system at the end of 2005. This system would provide a counterforce on the gas pedal

when the lead vehicle was getting too close. The force feedback pedal was also said to be

active when ACC is engaged so that drivers receive feedback on the controller activity

(Continental 2004).

In the haptic gas-pedal design for car-following support, haptic information of the

safe-field-of-travel boundaries provides them, first of all, with a complementary channel,

besides vision, to determine these boundaries. Second, continuous haptic presentation of

the boundaries enables continuous haptic perception of these boundaries. Third, by

presenting the haptic information through the gas pedal, a direct connection is created

between stimulus and response, that is, longitudinal control information is presented

through the longitudinal control channel in such a way that the stimulus is compatible

with the required response (Sheridan and Ferrell 1974, Wickens 1992).

Ergonomics 1711

Dow

nloa

ded

by [

Ston

y B

rook

Uni

vers

ity]

at 1

3:06

24

Oct

ober

201

4

Drivers can adjust their foot admittance, that is, their willingness to yield to forces,

depending on the task they are executing (Abbink et al. 2004). A normal, passive gas pedal

requires drivers to execute a position task (maintaining a constant position of the gas

pedal). Even during car-following, when drivers are not necessarily trying to maintain a

desired speed but rather a desired separation with the lead vehicle, gas pedal control

reduces the separation control task to a position control task of the gas pedal. Although

the driving task remains the same during car-following with haptic feedback (maintaining

inter-vehicle separation), the interpretation of the control task of the gas pedal may change

from a position task to a force task (maintaining a constant force on the gas pedal, by

yielding to force changes) by proper design of the haptic feedback logic.

The main objective of this study was to investigate the influence of haptic gas pedal

feedback on driver control behaviour during car-following. To this end, a car-following

experiment was conducted in a fixed-base driving simulator, in which drivers experienced

force feedback, stiffness feedback and no feedback (the baseline condition).

When using the haptic gas pedal, drivers were expected to adopt a force task strategy

to benefit from the provided haptic feedback. Based on the richer feedback content of

stiffness feedback, the force task strategy was furthermore expected to be more apparent

with stiffness feedback than with force feedback. If drivers adopted a force task, the

standard deviation of the measured pedal forces of the force feedback and stiffness

feedback conditions was expected to be significantly smaller than those of the condition

without haptic feedback.

Since the haptic feedback informed drivers of separation changes with the lead vehicle

through a complementary sensory modality, it was likely that the perceptibility of these

changes would increase. Both haptic feedback systems were expected, therefore, to yield

similar or better car-following performance compared to the baseline condition without

haptic feedback.

The stimulus-response compatibility of the haptic feedback signals with the desired

control action for maintaining a desired separation with a lead vehicle was expected to

increase the control sensitivity of drivers, leading to more efficient control activity.

2. Method

2.1. Experiment design

A repeated measures, between-subjects design was used, in which one independent

variable, S, was tested: no haptic feedback (Snone); force feedback (Sforce); or stiffness

feedback (Sstiffness).

During car-following, the lead vehicle speed of 100 km/h was disturbed with a crested,

stochastic sum of sinusoids with a maximum amplitude Ad of 10 km/h and a maximum

bandwidth fd of 0.5 Hz. This signal manifested itself to the participants as an

unpredictable lead vehicle speed disturbance.

In total, 20 intervals of 1 min driving were recorded for each participant, thus yielding

20 repetitions of the same driving condition per driver. To be able to detect any pairwise

difference in means in the dependent measures of at least 1 SD unit, with probability 0.90,

using Scheffe’s multiple comparison procedure with a ¼ 0.05, an estimated minimum

sample size of 26 per tested condition would be necessary (Pearson and Hartley 1951,

Levin 1975). With 20 repetitions per subject, the sample size obtained in this experiment

was assumed to be sufficient to be representative for the particular condition

(at least 120 samples per condition). Total driving time per participant was approximately

30 min.

1712 M. Mulder et al.

Dow

nloa

ded

by [

Ston

y B

rook

Uni

vers

ity]

at 1

3:06

24

Oct

ober

201

4

2.1.1. Dependent measures

Car-following performance, control activity and physical workload were the main

parameters of interest for this study. In order to determine car-following performance,

time headway (THW) and the inverse of time-to-contact, iTTC, were recorded

(Brackstone and McDonald 2007). THW can be interpreted as an estimate of the safety

margin, or safe-field-of-travel, drivers adopt during car-following (Boer 1999), since it

represents the maximum amount of time drivers have to react to a deceleration of the lead

vehicle (van Winsum and Heino 1996, Taieb-Maimon and Shinar 2001). Boer (1999)

characterises TTC as the amount of time drivers have to ‘intervene by employing a suitable

skill to avoid an impending crash’. THW and iTTC express car-following performance,

therefore, in terms of how well drivers can maintain their safe-field-of-travel, which is

directly related to what the haptic feedback system is intended to communicate. THW and

iTTC are defined as in Equations (1) and (2).

THW ¼xrel

vcarð1Þ

iTTC ¼�vrel

xrelð2Þ

with xrel, vrel, and vcar representing the relative distance, relative velocity and own car speed

respectively.

For control activity, the gas pedal position ap was measured. Mean and standard

deviation were determined off-line.

During car following, the fluctuations of the pedal force due to the haptic feedback

forces will be relatively small compared to the static component of the pedal force, that is,

the minimum force drivers have to exert on the gas pedal to depress the pedal to the

desired ap. The mean pedal depression force Fp – as an estimate of the average static

component of the pedal force – is, therefore, the biggest contributor to physical load. Since

there was no electromyography (EMG) equipment available during the experiment, it was

not possible to determine actual workload for each subject individually, as this depends on

the amount of muscle (co-)contraction. Instead, the mean Fp was used as an indicator of

the physical load.

2.2. Participants

A total of 21 participants between the ages of 24 and 30 years (mean age 26 + 1.9; two

females and 19 males) participated voluntarily in the experiment. Six subjects (one female;

five males) were given the baseline condition without the haptic feedback system (Snone); eight

subjects (all male) were given the force feedback system (Sforce); and seven subjects (one

female; six males) were given the stiffness feedback system to drive with (Sstiffness). All subjects

had a valid driver’s licence and an average driving experience of 7 + 2.2 years, driving on

average three short trips per week. None of the participants was visually or otherwise

physically impaired such that it could influence their driving performance in the simulator.

2.3. Apparatus

The experiment was conducted in a fixed-base driving simulator (see Figure 1a) equipped

with an actuated gas pedal (see Figure 1b) capable of delivering the force and stiffness

Ergonomics 1713

Dow

nloa

ded

by [

Ston

y B

rook

Uni

vers

ity]

at 1

3:06

24

Oct

ober

201

4

feedback as determined by the haptic feedback logic (see Mulder 2007, for details on

the implementation). The brake pedal and steering wheel were passive, as in a normal car.

The simulated vehicle was a heavy, but powerful vehicle with automatic transmission. The

driving scene was projected on the laboratory wall at a distance of 2.9 m from the driver-

eye reference point. The dimensions of the projected driving scene were 3.3 m 6 2.1 m,

resulting in a 608 horizontal and 408 vertical field-of-view. The graphical resolution of this

projection was 1280 6 1024 pixels. Engine sounds, tyre rumble and wind sounds were

audible from a surround sound system.

An in-vehicle sensor system was simulated, capable of providing distance and speed

information of leading vehicles (see, for example, Gandhi and Trivedi 2006 for a novel

environment detection and mapping technique as well as a comprehensive literature survey

of existing environment sensing systems). This information, together with the speed of own

vehicle, was used as input to the control logic that determines the haptic feedback cues that

are to be presented to the driver. Data acquisition was done at 50 Hz.

2.4. Procedure

Prior to the experiment, drivers were given a written task instruction. They were asked to

follow the lead vehicle at a prescribed separation of 1.5 s THW, a slightly higher than

average THW as measured, for example, by Piao and McDonald (2003) on motorways in

Europe. To help drivers drive at the required THW separation, a partially translucent

rectangle, or THW-indicator, was shown at an exact THW of 1.5 s for 10 s before data

recording started. When the rear bumper of the lead vehicle touched the THW-indicator,

the separation between own vehicle and the lead vehicle was exactly 1.5 s. After the

THW-indicator disappeared, data were recorded for the next 60 s, after which the

THW-indicator appeared again. This visual ‘calibration’ method was applied to avoid

cumulative drift in inter-vehicle separation between repeated measures during the

experiment. Cumulative drift in THW would make comparison of data between drivers

and even within drivers much more difficult or would require considerably longer periods

of driving per participant. In Figure 2, the general idea of the repeated measurements and

the THW calibration is visualised.

Figure 1. Photo of the fixed base driving simulator setup (a) and details of the actuated gaspedal (b).

1714 M. Mulder et al.

Dow

nloa

ded

by [

Ston

y B

rook

Uni

vers

ity]

at 1

3:06

24

Oct

ober

201

4

Drivers were explicitly instructed to maintain separation with the lead vehicle for as long

as possible with the gas pedal only. No specific instructions were given on how to use the

haptic feedback in case of the force feedback and stiffness feedback systems (that is, no explicit

mention was made of force-task or position task strategies). All drivers were given 5 min of

driving time to familiarise themselves with the driving simulator and the driving task.

3. Results

Measured data were analysed using univariate ANOVA statistics. Findings with p 5 0.01

were considered significant differences. Scheffe’s post-hoc analysis (a ¼ 0.05) was used to

determine the homogenous subsets. The data distributions were examined for normality to

ensure the applicability of the ANOVA method and thus the validity of the statistical

results presented.

The THW deviated from the instructed 1.5 s. From Figure 3a it can be seen that

drivers who used the haptic feedback system (Sforce; Sstifffness) increased their mean THW

compared to the baseline condition Snone. These increases are significant for each condition

(F2,397 ¼ 19.856; p 5 0.01). The standard deviation of THW, sTHW, is significantly lower,

however, for Sforce and Sstiffness compared to Snone (F2,397 ¼ 4.647; p 5 0.01) as can be seen

in Figure 3b.

The standard deviation of iTTC, siTTC, is significantly different for each S

(F2,397 ¼ 38.785; p 5 0.01), with Snone having the highest and Sstiffness the lowest value,

see Figure 4.

The control activity displayed by drivers to deliver the determined following

performance is reduced significantly when the haptic feedback system is active

(F2,397 ¼ 40.721; p 5 0.01). Especially, Sstiffness yields a great reduction in the standard

deviation of the gas pedal depression (sap) – to almost half of the value found for Snone and

a third of that for Sforce, see Figure 5. Furthermore, the mean of the gas pedal depression

ap is also significantly (F2,307 ¼ 39.359; p 5 0.01) lower for Sforce and Sstiffness compared to

Snone, with Sstiffness again having the lowest value. Even though the mean ap is the lowest

for Sstiffness, due to the small standard deviation this also yields to the least amount of time

tr for which the gas pedal was fully released. Compared to Snone, both Sforce and Sstiffness

yield significantly lower tr (F2,397 ¼ 48.477; p 5 0.01).

The mean measured pedal force Fp, an indication of the physical workload, increased

significantly for Sforce and Sstiffness (F2,397 ¼ 145.134; p 5 0.01). Sforce yielded significantly

Figure 2. Visual representation of the construction of an experiment run. At the start of the run,the time headway (THW)-indicator is visible. When the driver and the lead vehicle are following atstationary speeds, the data acquisition is started. The THW-indicator disappears and the lead vehiclespeed is disturbed. After an experiment dependent time interval, the disturbance is stopped and theTHW-indicator appears again for 10 s. Then, the lead vehicle speed is disturbed again. This processis repeated 20 times to yield 20 repeated measures of the same condition.

Ergonomics 1715

Dow

nloa

ded

by [

Ston

y B

rook

Uni

vers

ity]

at 1

3:06

24

Oct

ober

201

4

higher pedal forces than Sstiffness, although the difference is only approximately 0.5 N, see

Figure 6a. The standard deviation of the pedal force, sFp, for Sforce and Sstiffness is also

significantly higher than for Snone (F2,397 ¼ 184.861; p 5 0.01), with Sstiffness having the

lowest value, see Figure 6b.

4. Discussion

The addition of haptic feedback had significant effects on all dependent measures under

the conditions tested. Overall, the results of the experiment indicate that small

improvements in car-following performance were achieved with less control activity in

the presence of haptic feedback. The mean measured pedal forces, however, increased due

to the haptic feedback forces.

Figure 3. The means (horizontal lines) and 95% CI (grey bars) of the mean measured time headway(THW) (a) and standard deviation of THW (b) for all subjects.

Figure 4. The means (horizontal lines) and 95% CI (grey bars) of the standard deviation of themeasured inverse time to contact (iTTC) for all subjects.

1716 M. Mulder et al.

Dow

nloa

ded

by [

Ston

y B

rook

Uni

vers

ity]

at 1

3:06

24

Oct

ober

201

4

Despite the fact that drivers drove significantly further from the THW than they were

asked to drive (1.5 s) for Sforce and Sstiffness, the standard deviations of THW and iTTC

remained lower than for Snone. With the higher mean THW, the visual effects of changes in

lead vehicle separation would have been less salient. One may expect that this leads to an

increase in sTHW, since reaction to THW differences becomes more difficult. This

decreased visual saliency, however, is more than offset by the haptic feedback. In these

conditions, a lower sTHW was found, meaning a more accurate tracking of the lead vehicle.

It must be noted here, though, that these simulator results yield sTHW’s approximately 0.1–

0.2 s smaller than measured (for all S), for example, on highways in Europe by Piao and

McDonald (2003), even though their corresponding measured average THW is

approximately 0.5–0.6 s lower than in the driving simulator (for all S). Thus, the driving

simulator environment and/or task already seems to have an increased perceptibility of the

changes in THW. This increase in perceptibility of the changes in THW in the simulator

Figure 5. Mean measured gas pedal position, ap, vs. standard deviation of ap. Bubble size indicatesthe percentage, tr, of the total experiment time the gas pedal was released completely.

Figure 6. The means (horizontal lines) and 95% CI (grey bars) of the mean measured pedal force,Fp (a) and the standard deviation of Fp (b) for all subjects.

Ergonomics 1717

Dow

nloa

ded

by [

Ston

y B

rook

Uni

vers

ity]

at 1

3:06

24

Oct

ober

201

4

environment might be caused by the experiment conditions, which required of the drivers

mainly to focus on the car-following task. In real-life driving situations, drivers will not be

focused on car following alone.

More pronounced effects are found for the control activity. With haptic feedback,

drivers displayed far less control activity than without haptic feedback. Especially, Sstiffness

yielded very low control activity compared to Sforce and Snone. Thus, drivers seem to be

controlling their vehicle in a much more relaxed manner without, however, sacrificing

following performance. This also leads to less time during which the pedal is not

depressed, resulting in longer periods of time during which they can benefit from the

information that is provided haptically. In addition, the standard deviation of the

measured pedal force was also lowest for Sstiffness. The combination of low control activity

and low variation in measured pedal forces is especially salient in Sstiffness. These results are

in accordance with the results of neuromuscular measurements of the foot while

controlling a gas pedal as presented by Abbink (2006). Those results show that the

presence of haptic gas-pedal feedback during car following increases the ankle–foot

admittance (that is, the causal relationship between position and force). A larger

admittance means that drivers gave way to the feedback forces more (adopting a force

task), instead of resisting them (adopting a position task). It appears that drivers adopted

a force task strategy in controlling the gas pedal when receiving haptic feedback. The force

task strategy is more salient for Sstiffness.

The benefit of a force-task strategy is found in the increased admittance, which

inherently increases the influence of the feedback forces on the pedal deviations. The

increased admittance is partly realised by increased reflexive muscle activity, which allows

for much faster reactions to changing forces (de Vlugt et al. 2002, Abbink et al. 2004), and

thus to changes in the car-following situation. Note that drivers can choose to change their

admittance: if they do not agree with the feedback forces, they can easily resist them.

Adopting a force-task strategy in controlling the gas pedal is possible only when there is a

continuous presentation of feedback forces, unlike binary types of haptic feedback such as

force pulses or vibrations. Furthermore, the continuous exposure to the feedback forces

increases driver familiarity with the system and explicitly communicates that the system is

operational, thereby implicitly announcing the boundaries of the safe-field-of-travel.

The substantial reductions in control activity indicate that the most beneficial effect of

the tested haptic feedback systems seems to be increased driver control comfort during car

following. Some caution should be exercised in ascribing these results entirely to the

presence of haptic feedback. The absence of motion in the driving simulator, and hence the

absence of the comfort-decreasing effect of fluctuating accelerations, has most likely

influenced the results. In a moving-base simulator or in a real car, the difference in control

activity for Snone and Sforce and Sstiffness is expected to be smaller. Rockwell (1972) indicates

that humans can sense as little as +0.01 g of vehicle acceleration. This is well below the

accelerations drivers would have experienced (in the order of +0.04 g) had the simulator

been able to provide motion. Because the vehicle dynamics filtered out most effects of the

high frequency inputs from the gas pedal, drivers would, with motion cues, have been able

to notice the ineffectiveness of their high frequency control actions better and thus have

less high-frequency control variation in the Snone configuration, making the differences

between this condition and the Sforce and Sstiffness conditions smaller.

The measured mean pedal forces were found to be higher for the haptic feedback

conditions, indicating that the physical workload might have been higher with haptic

feedback. Abbink (2006), however, shows that, energy-wise, it is favourable to have a

static component of the pedal force that is at least as high as the exerted force of the foot/

1718 M. Mulder et al.

Dow

nloa

ded

by [

Ston

y B

rook

Uni

vers

ity]

at 1

3:06

24

Oct

ober

201

4

leg combination of the driver at those pedal depressions that are used most during driving

(approximately 15–30% depression to maintain highway speeds). For pedal forces lower

than 30–60 N, their experimental results indicate that drivers would have had to co-

contract both the shin muscles and calf muscles to stabilise the desired pedal position. This

co-contraction of muscles leads to unnecessary fatigue. The higher mean pedal forces

measured in the experiment described in this article might therefore actually have

decreased rather than increased the physical workload of drivers, since the measured mean

pedal force was shown to be far lower than 30 N for all conditions tested. Without EMG

measurements of the appropriate muscles, this remains a matter of speculation, however.

5. Conclusions

The results of the experiment show that haptic gas-pedal feedback can be an effective and

comfortable means of providing drivers with continuous support information during car-

following. Small improvements in car-following performance were found. Drivers

appeared to have increased their control sensitivity to separation changes in the following

situation, while relaxing their control behaviour. Haptic feedback thus seems to fit the

response automation category mentioned by Goodrich and Boer (2000).

From the discussed options of manipulating the gas-pedal feedback, either force

feedback or stiffness feedback, it appears that for the conditions tested in the experiment,

stiffness feedback is the most promising feedback option.

The drivers were not explicitly instructed to adopt a force-task in the car-following

context of this experiment. The results indicate, and previous research has confirmed

(Abbink 2006, Mulder 2007), that drivers do tend to adopt a force-task strategy when

presented with continuous force or stiffness feedback. This result suggests that a force-task

strategy is a natural task interpretation of drivers when exposed to force or stiffness

feedback information during car following.

Since the haptic feedback was only tested at a single THW, the results cannot easily be

extrapolated towards more dangerous situations, with smaller THW. Because vision will

be increasingly more dominant at smaller THW due to the better perceptibility of changes

in lead vehicle separation, the effectiveness of haptic feedback in these circumstances will

most likely be less significant, though the results presented here are encouraging.

Furthermore, the effects of haptic feedback have been tested only under continuous car-

following conditions. In reality, however, drivers will not be engaged in car following

continuously and will, therefore, not be exposed to the haptic feedback information

continuously, as the feedback is only generated when a lead vehicle is within following

distance.

Future research will, therefore, have to investigate whether continuous haptic gas-

pedal feedback can also be effective in more pressing situations (such as smaller THW), in

which drivers are required to take quick corrective control actions to prevent the car-

following situation from escalating to a potential collision situation. The saliency of the

haptic feedback signal will then become much more important. The transition between

these two situations will be an important test case for haptic feedback to be effective in the

whole range of situations from car-following to collision avoidance.

Acknowledgements

This research was sponsored by the Nissan Motor Company Ltd., Japan, as part of an internationalmulti-university research effort on driver support systems.

Ergonomics 1719

Dow

nloa

ded

by [

Ston

y B

rook

Uni

vers

ity]

at 1

3:06

24

Oct

ober

201

4

References

Abbink, D.A., 2006. Neuromuscular analysis of haptic gas pedal feedback during car following. Thesis(PhD). Delft University of Technology.

Abbink, D.A., van der Helm, F.C.T., and Boer, E.R., 2004. Admittance measurements of the footduring ‘maintain position’ and ‘relax’ tasks on a gas pedal. In: Proceedings of the IEEEinternational conference on systems, man, and cybernetics, 10–13 October 2004, Den Haag,the Netherlands. 2519–2524.

Brackstone, M. and McDonald, M., 2007. Driver headway: how close is too close on a motorway?Ergonomics, 50, 1183–1195.

Boer, E.R., 1999. Car following from the driver’s perspective. Transportation Research – Part F:Traffic Psychology and Behaviour, 2, 201–206.

Continental, 2004. Active distance support: ACDIS [online]. Available from: http://www.conti-online.com/generator/www/de/en/cas/cas/themes/products/powertrain_and_chassis/chassis_control_systems/download/download_acdis_en.pdf [Accessed 27 March 2008].

de Vlugt, E., Schouten, A.C., and van der Helm, F.C.T., 2002. Adaptation of reflexive feedbackduring arm posture to different environments. Biological Cybernetics, 87, 10–26.

Dingus, T.A., et al., 1997. Human factors field evaluation of automotive headway maintenance/collision warning devices. Human Factors, 39, 216–229.

Gandhi, T. and Trivedi, M.M., 2006. Vehicle surround capture: survey of techniques and a novelomni-video-based approach for dynamic panoramic surround maps. IEEE Transactions onIntelligent Transportation Systems, 7, 293–308.

Godthelp, J. and Schumann, J., 1993. Intelligent accelerator: an element of driver support. In:A.M. Parks and S. Franzen, eds. Driving future vehicles. London: Taylor & Francis, 265–275.

Goodrich, M.A. and Boer, E.R., 2000. Designing human-centered automation: tradeoffs in collisionavoidance system design. IEEE Transactions on Intelligent Transportation Systems, 1, 40–54.

Hjalmdahl, M., Almqvist, S., and Varhelyi, A., 2002. Speed regulation by in-car active acceleratorpedal – effects on speed and speed distribution. IATSS Research, 26, 60–67.

Lees, M.N. and Lee, J.D., 2007. The influence of distraction and driving context on driver responseto imperfect collision warning systems. Ergonomics, 50, 1264–1286.

Levin, J.R., 1975. Determining sample size for planned and post hoc analysis of variancecomparisons. Journal of Educational Measurement, 12, 99–108.

Mulder, M., 2007. Haptic gas pedal feedback for active car-following support. Thesis (PhD). DelftUniversity of Technology.

Pearson, E.S. and Hartley, H.O., 1951. Charts of the power function for analysis of variance tests,derived from the non-central F-distribution. Biometrika, 38, 112–130.

Piao, J. and McDonald, M., 2003. Low speed car following behaviour from floating vehicle data. In:Proceedings of the IEEE intelligent vehicles symposium, 9–11 June 2003, Columbus, Ohio, USA.462–467.

Rockwell, T.H., 1972. Skills, judgment and information acquisition in driving. In: T.W. Forbes, ed.Human factors in highway traffic safety research. New York: Wiley-Interscience, 133–164.

Rudin-Brown, C.M. and Parker, H.A., 2004. Behavioural adaptation to adaptive cruise control(ACC): Implications for preventive strategies. Transportation Research Part F, 7, 59–76.

Sheridan, T.B. and Ferrell, W.R., 1974. Man-machine systems: Information, control, and decisionmodels of human performance. Cambridge, Massachusetts: MIT Press.

Taieb-Maimon, M. and Shinar, D., 2001. Minimum and comfortable driving headways: Realityversus perception. Human Factors, 43, 159–172.

van Winsum, W. and Heino, A., 1996. Choice of time-headway in car-following and the role of time-to-collision information in braking. Ergonomics, 39, 579–592.

Verwey, W.B., et al., 1993. GIDS functions. In: J.A. Michon, ed. Generic intelligent driver support.London: Taylor & Francis, 113–146.

Wickens, C.D., 1992. Engineering psychology and human performance. New York: HarperCollins.

1720 M. Mulder et al.

Dow

nloa

ded

by [

Ston

y B

rook

Uni

vers

ity]

at 1

3:06

24

Oct

ober

201

4