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Int. J. Human-Computer Studies 66 (2008) 125–131

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Editorial

Designing and evaluating driver support systems with the user in mind

Pierre Fastreza,�, Jean-Baptiste Haueb

aDepartement de Communication, Universite Catholique de Louvain, Louvain-la-Neuve, BelgiumbYuseo, 13 Place Kossuth, 75009 Paris, France

Abstract

In this editorial to the special issue we first introduce the field of driver support system (DSS) design and evaluation, frame it into the

larger context of human–computer interaction research, and highlight some of its specificities. We then proceed to briefly present the

selection of articles that compose this issue. Finally, we put the contributions to the special issue into perspective along a number of

dimensions to show how they represent the diversity of current DSS research.

r 2008 Elsevier Ltd. All rights reserved.

Keywords: Driver support systems; Driver-centered design; Evaluation; Editorial

1. Introduction

The design of vehicles has long been an object of studyfor engineers and for human factors specialists alike. Thelast two decades have seen the rise of driver supportsystems (DSS), embedded computer systems that managesome of the vehicle’s functions or modify how driversinteract both with their vehicle, the road and ongoingtraffic. This goal of supporting the driver has naturallyresulted in a driver-centered approach of DSS, which candirectly be related to the larger trend of user-centereddesign (Norman and Draper, 1986).

The development of DSS has brought a wealth of newresearch questions to the field of vehicle design. As in otherareas of user-centered design—such as computer supportedcollaborative work (CSCW), computer mediated commu-nication (CMC), etc.—these new research questions havebeen addressed by researchers from both the engineeringsciences and the social and behavioral sciences. Thisdynamic community integrated the study of DSS into thefield of human–computer interaction (HCI) and has put theinteraction between driver, vehicle and context underscrutiny. In the field of HCI, the study of DSS nowbelongs to the wider ‘mobile and ubiquitous computing’

e front matter r 2008 Elsevier Ltd. All rights reserved.

cs.2008.01.001

ing author. Tel.: +3210 47 29 14; fax: +3210 47 30 44.

esses: pierre.fastrez@uclouvain.be (P. Fastrez),

(J.-B. Haue).

line of research (and has contributed to making it aprominent research area), which brought engineers andsocial and behavioral scientists closer together. Over theyears, the scientific landscape has evolved to reflect thisevolution. For instance, engineering conferences dedicatedto ubiquitous and mobile computing (such as Ubicomp,EUC, ITSC, etc.) have integrated HCI tracks, HCIconferences have developed their mobile side (e.g. HCIInternational 2007 featured an ‘‘Environment, Transporta-tion and Safety’’ section), and new conferences focused onmobile and embarked technology (such as Mobile HCI)have flourished worldwide.As an object of study for HCI, DSS present several

striking specificities.The first DSS specificity is a combination of both strong

safety and usability requirements. On the one hand, HCI nowhas a long history of studying highly specialized systems withhigh-safety stakes, such as nuclear power plants (Stanton,1996; Roth, 1997; Carroll et al., 2001), subway control rooms(Heath and Hindmarsh, 2000), air traffic control rooms(Bentley et al., 1992; Connelly et al., 2001), or airplanecockpits (Wiener, 1988; Sarter and Woods, 1992, 1994). Onthe other hand, numerous HCI studies have focused theirattention on more mundane systems, like personal computerswith direct manipulation interfaces (Hutchins et al., 1987;Frohlich, 1993; Schneiderman, 1997).The systems embedded in common contemporary cars

are distinctive in that they combine the safety stakes of the

ARTICLE IN PRESSP. Fastrez, J.-B. Haue / Int. J. Human-Computer Studies 66 (2008) 125–131126

former with the wide range of users of the latter, bringing anew twist to the issue of safety. Indeed, in addition to thenecessity to preserve safety at all cost, DSS must also beeasy to learn, as most of their users are not trained expertsor professional drivers: drivers must be able to use them‘out of the box’, which means they need to be explicit abouthow to interact with them, behave according to driverexpectations, and handle operator errors appropriately.Furthermore, they cannot interfere with the driver’sactions, and need to avoid conflicts with the driver’sperception of the situation at hand. This last requirementimplies that in general, DSS must conform to the waydrivers think of the situations they are used in, and providedrivers with information that helps them to construct acorrect understanding of the current situation.

A second DSS particularity is that they are integratedinto a larger context of activity. Unlike other real-timesystems with high-safety stakes, interacting with the DSS isnot the driver’s primary task, which is of course driving.Hence, the tradeoff between the cognitive load implied bysystem use and the benefits of using the system must bethoroughly evaluated. Of course, attention is only onedimension of the larger question of how DSS are integratedwithin the system composed of the driver, their vehicle andthe surrounding (road and traffic) context. The variety interms of road infrastructure, vehicles and driver profiles,but also the influence of traffic, cultural values and even ofdrivers’ everyday preoccupations results in a huge diversityof situations of use in comparison (for instance) with theactivity of conducting nuclear plant, which is extremelyformalized, follows a strict protocol and takes place in aregulated, safety-aware culture. This diversity complicatesthe work of establishing DSS specifications and planningthe integration of DSS into the driver’s environment.

There has been a chasm between two approachestowards this integration. The first approach relies on theuse of automation: such systems support drivers byallowing them to delegate a part of the driving task tothe system. In the second approach, systems supportdrivers by helping them stay ‘in the loop’, drawing theirattention to critical aspects of their driving activity. In thiscase, drivers rely on information provided by the system todo the driving.

A third specificity of DSS is their position as a secondaryfeature that is not only intended to increase safety, but alsoto boost the competitive attractiveness of vehicles for massmarket audiences. As their use is not mandatory, DSS firsthave to be adopted by their users. System reliability andlearnability are among the factors that impact systemadoption. So are trust and enjoyment. Trust in automationhas been thoroughly studied by the human factorscommunity (Muir, 1994; Muir and Moray, 1996; Lee andSee, 2004), and may be critical to system adoption forsystems designed in both approaches mentioned above:Drivers must be able to trust the DSS in order to decideto delegate (for control-taking systems) or to rely onprovided information (for systems that keep them

involved). Enjoyment is a more recent object of interestin HCI (‘‘emotional design’’ is starting to become animportant dimension of it—Norman, 2004), but whatseemed like icing on the cake in the past has slowly becomea standard requirement for today’s systems. In order to beadopted, not only do DSS need to be usable, learnable andtrustable: they also must make driving a more pleasantexperience.

2. Enhancing the driving experience: DSS design and

evaluation

The design of a DSS that makes driving both safer andmore enjoyable is a complex process. This is in part due tothe fact that it implies the reconfiguration of a larger pre-existing system that is composed of the driver, the vehicleand all its technology, the surrounding traffic and the roadcontext. This system already has its own subtle equili-briums, which the DSS may perturb.Since modifying this larger system may have dire

consequences on safety, DSS must be thoroughly evaluatedbefore they hit the streets. This evaluation cannot limititself to testing the system’s robustness or usability inisolation: it must elucidate how the system affects existingdriving processes. As a matter of fact, it is not the DSSitself that must be evaluated. What must be assessed is howthe larger ‘‘driver-car-traffic’’ system will behave with theaddition of a given DSS. Among the things that need to bedocumented are the way the DSS’s actions interfere withpreviously stabilized processes, the effect of context onDSS use and how the interaction with the DSS co-existswith the interaction with other devices in the vehicle.More fundamentally, the necessity of a thorough

evaluation of the system justifies the need for a deepunderstanding of the way human beings function in a car,i.e. how they behave, and what models they hold of thetechnological artifacts they use. This understanding canhelp answer multiple questions regarding the differentinterconnected aspects of DSS design such as what type ofinformation they should provide to the driver, or whatperceptual modality should be used to convey thisinformation. As to perceptual modalities, visual andauditory signals seem to have dominated so far, althoughhaptic feedback has now been explored for a number ofyears. Another set of question relates to the semiotic andtemporal properties of the representations generated by thesystem: should they be iconic (i.e. resemble what they referto) or symbolic (i.e. rely on abstraction and conventionaldepictions or on language)? Should they rely on meta-phors? Should they be discrete (e.g. binary signals) orcontinuous (constantly updated information flows)?Studying driver behavior may also guide the way theserepresentations are integrated in the vehicle (e.g. on thedashboard, in a heads-up display, etc.) or specify the timingof their availability (e.g. how much time in advance must awarning message be produced?). Additionally, such knowl-edge may help determine what the system’s attentional

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demands are, and what cognitive load it represents forits user.

Understanding driver behavior and cognition is thus anecessity for DSS design and evaluation, which are verymuch like two sides of the same coin. The study of driverbehavior and cognition is achieved through the use of agreat variety of methodological approaches, focused on theobservation of drivers interacting with new and existingsystems in a number of different contexts. One of the mostsalient dimensions along which these methodologicalapproaches vary is the degree of control the researcherhas on driver–system interaction, which can be described asa continuum. At one end of the continuum, laboratorystudies represent the most controlled research environ-ments in this context. They focus on the study of generalhuman cognition and perception mechanisms (such asattention or vision) that underlie driving. A little furtheralong the same continuum, simulator studies confrontdrivers with pre-established scenarios they must enact in avirtual world that ensures all participants encounter thesame events. Studies completed on closed circuits representthe next step on our continuum, as they allow drivers tointeract with the system in actual vehicles, but with limitedand regulated traffic. Finally, real-world studies takedrivers on the road and observe how they perform in‘‘natural’’ settings. Such studies can be carried out eitherwith existing technology prior to the design of prototypes,or with novel DSS once they have passed tests in morecontrolled environments. In this regard, the continuumgoing from controlled to fully natural observation envir-onments corresponds to the most common scenario for theevaluation of a given novel DSS, from prototype testing insimulators (with no consequence on driver safety in casesof system failure) to final usage evaluation before thesystem is released on the market.

3. A brief presentation of the contributions to this issue

The contributions that compose this special issue ondriver-centered design tackle most of the questions and relyon several of the methodological approaches we haveoutlined above. They can be gathered in three groups,according to their main object of study.

The first group of contributions is composed of threestudies that evaluate prototypes of DSS that convey

information to the driver in novel ways. The first of them,the contribution of Caird et al. (2008) evaluates the effectof two in-vehicle signs (IVS) presented in a head-up display(HUD) format on younger and older driver’s performanceat intersections. The in-vehicle signs, as they warn driversof upcoming light changes in advance, are expected toassist drivers in making decisions and hence to reducecrashes at intersections. Results presented by the authorsshow that the primary effect of the IVS was to cause driversto remove their foot from the accelerator pedal in advancewhen approaching an intersection, causing the averagespeed of approach to be lower with IVS than without them.

Younger drivers looked at the in-vehicle signs more oftenand longer than older ones. Older drivers were less likely tostop and more likely to go through the intersection thantheir younger counterparts. Finally, in both age groups,more drivers stopped at intersections when using IVS.Isabelle Milleville’s article (2008) tackles a common

problem for drivers of contemporary cars equipped withsystems that cancel vestibular information. As these driversdo not feel lateral acceleration in bends, they may experiencedifficulties in adapting the way they steer to the roadcurvature. Milleville-Pennel hypothesizes that this loss ofvestibular information can be compensated by tilting thedriver’s visual frame of reference, thereby using a differentsensory channel to convey information to the driver. Thishypothesis was tested in an experiment using a high-fidelitysimulator, where the rectangular visual frame of thewindshield was distorted to a trapezoidal shape in order tosimulate the inclination of the car. Participants drovethrough a scenario involving curves of different strengths,either with an external frame tilt (in accordance with theload transfer of a real-world car), with an internal tilt, orwithout any tilt. Results showed that the effect of the frametilt depended on whether it was perceived consciously orunconsciously: uninformed participants tended to driveslower in the tilted frame conditions (compared to theno-tilt condition), whereas informed participants tended todrive faster in the external tilt condition. Contrary topredictions, neither participant field dependence nor roadgeometry had a significant influence on driving behavior.In their paper, Li and Milgram (2008) explored how

optical looming (the symmetrical expansion of the retinalimage of an approaching object size) could be applied tothe brake lights of a lead vehicle in order to reduce the riskof rear-end collision. Their simulator prototype allows forthe dynamic manipulation of optical looming cues of a leadvehicle’s brake lights, the separation and size of whichchange as a function of time to collision (TTC). This meansthat the lead vehicle can be made artificially perceivedcloser (or further) by enlarging and bringing closer (ordecreasing in size and moving away) its brake lights. Thesystem thereby provides the driver with cues about thedeceleration rate of the lead vehicle they are following, acritical type of information in cases of hard braking, orbraking on short distances. The authors predicted a changein brake timing proportional to the size of the manipula-tion. This hypothesis was put to the test in a simulatorunder three different visibility conditions: daytime, night-time with following vehicle headlights and nighttimewithout following vehicle headlights. The experimentincluded manipulation of brake lights in both directions(i.e. expansion and contraction), with two levels ofmagnitude for each (in addition to a no-manipulationcondition). The visibility conditions proved to have aneffect on braking behavior: participants removed their footfrom the gas pedal faster in the nighttime with lightscondition than in other conditions. The optical loomingmanipulation also had an effect in terms of time of first

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pressing brake pedal, time of maximum braking force,maximum braking force and minimum following distances:subjects braked sooner in the expansion conditions andlater in the contraction conditions, in proportion of themagnitude of the manipulation. These effects were mostlysignificant in the nighttime viewing conditions.

The next group of papers in this special issue representsa shift of attention from driver behavior and perfor-mance towards driver perception of technology: trust,acceptance, attitude towards DSS, and their consequenceson DSS use.

Leila Takayama and Clifford Nass’ contribution (2008)revolves around a voice-based DSS. Voice-based systemsrepresent an alternative to visual signals, which may placetoo much demand on the driver’s visual attention. Thetheoretical stance that underlies the authors’ hypotheses isthat computers are perceived as social actors by humanswho interact with them. Hence, the way drivers interact,respectively, with proximate and distant systems shouldexhibit the same differences as between the way theyinteract with passengers vs. remote conversationalists:in both cases the feeling of lack of shared context andcommon ground could require more effort on thepart of the driver to interact with the distant systemor person. In the experiment reported in this paper, avoice-based system was presented to the subjects as beingeither located in the vehicle, or distant and connectedwirelessly. It was then used in a simulator on one of twoprogrammed courses with similar events where it provideddrivers with information on upcoming road events, andprompted them to disclose personal information. Theperceived location of the information source was expectedto have an effect both on driver behavior and on theirengagement with the system. Indeed results show that whenthe DSS was presented as an in-car system, it causeddrivers to drive faster but to feel more engaged with thesystem. These two effects counterbalanced each other, asthe former made their driving less safe, and the latter madeit safer.

In their article, Rajaonah et al. (2008) investigate howadaptive cruise control (ACC) use is impacted by severaldriver-related variables, namely trust, perceived workloadand perceived risk associated with ACC usage. The authorspresent a theoretical framework inspired by Riley’s (1996)theory of operator reliance on automation, which defines aweb of relationships between ACC use (their independentvariable) and overall trust, trust in ACC, trust incooperation between the driver and the system, self-confidence, perceived workload and perceived risk (theirdependent variables). Participants in the experiment theyreport on were asked to drive on a simulator course withboth motorway and major road sections, and use the ACCas desired. The driver sample was then split into twogroups post hoc based on ACC use: high-use drivers andlow-use drivers. Using both raw car data recorded duringthe task and responses from questionnaires, the authorsevaluated how high-use drivers and low-use drivers were

different in terms of cooperation with the ACC and theirassessments of trust, perceived workload, and perceivedrisk. As far as cooperation with the system is concerned,high-use drivers were found to modify the reference speedand time headway more frequently than low-use driverswhen using the ACC. Although a number of correlationsappeared between the different aspects of trust, perceivedworkload and perceived risk, only two negative correla-tions between these dependent variables and ACC use weresignificant: ACC use on motorway was correlated withperceived risk (i.e. low-use drivers perceived ACC use onmotorway as more risky than high-use drivers did), andACC use on major roads was correlated with perceivedworkload (i.e. low-use drivers perceived ACC use onmajor roads as representing more workload than high-usedrivers did).Whereas the previous contributions focused on DSS

evaluation, the last article puts the design process itself intothe spotlight. Lee et al. (2008) report on the design andevaluation of maps optimized for vehicular environments(MOVE), an in-car navigation display that providescontext-relevant navigational information to the driverthrough the ongoing modification of map display. Thedesign was based on an initial review of existing systemsand three pilot studies that investigated how people givedirections and what their direction preferences are depend-ing on the type of place they are navigating to. On thisbasis, a preliminary version of MOVE implementedabstraction techniques used by people to draw route maps.The authors subsequently conducted a study on visualsearch behavior to evaluate how different renditionsof map features affect visual search, whether these fea-tures are targets or distracters. Results showed thatsemantic renditions (involving text and numbers) yieldedfaster response times than symbolic ones, and so didcolored targets compared to black and white targets (whencoupled with black and white distracters). These prelimin-ary studies allowed Lee and his colleagues to formulategeneral design principles for MOVE regarding displaychoices, representation of navigational information anddynamic optimization of information display. Theseprinciples in turn yielded four alternative versions ofMOVE including one or two of the following features:‘‘zoom in context’’, ‘‘route scrolling’’ and ‘‘small over-view’’. Their respective performances were compared withan existing (off-the-shelf) system. The experiment aimed atassessing whether interacting with MOVE as a secondarytask (in addition to the primary driving task) would resultin fewer glances and smaller fixation times (comparedto the existing system), leading to potentially safer driving.Results showed that all versions of MOVE outper-formed the existing system. The authors conclude ontwo guidelines for DSS design: semantic renditions shouldbe used for important target areas, and symbolic onesfor less important areas, and contextually optimizeddisplays significantly reduce driver workload and attentionrequirement.

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4. Diversity in focus, methods and design implications

The research carried out in the field of DSS design andevaluation is diverse in a number of ways. In order tohighlight how the contributions of this issue represent thisvariety, this section puts them into perspective according tothe following dimensions: object of the study, methodolo-gical orientation, nature of the collected data and relation-ship with design.

The distinction in terms of object of study emphasizes thefocus of the research in terms of factors1 that influencedriver performance when using a given DSS. A roughcategorization discriminates between three types of factors:technological, driver-related and context-related. Techno-logical factors are directly related to the system itself, or itsinteraction with other technological devices. For example,the issue of the form of representations generated by thesystem is evaluated in depth by Lee et al. (2008) when theycompare concurrent versions of their MOVE system.

In the case of driver-related factors, the focus is either ondriver perception, action and cognition in general or ondriver variability. The visual search study that Lee et al.(2008) report on can be placed in the former category. Thecontribution of Caird et al. (2008) is a good example of thelatter, as it seeks to evaluate the effect on their in-vehiclesign system on younger and older drivers.

Context-related factors include road type, traffic orweather conditions. Several articles in this issue focus onsuch factors, although most of the time not in isolation.Rajaonah et al. distinguish between motorways and majorroads to assess the effect of their ‘‘intervening’’ (driver-related) variables on ACC use. Li and Milgram (2008)observe the effects of their brake lights optical loomingsystem on braking behavior in different visibility condi-tions. As these two examples show, in most cases,technology-related variables, driver-related variables andcontext-related variables are studied in combination. Intheir contributions to this issue Milleville-Pennel (2008)and Caird et al. (2008) present research that integrates allthree types of factors. Milleville-Pennel (2008) looks at howframe tilt type (technology), awareness of the manipulationand field dependence (driver), and road geometry (context)affect driver behavior. Caird et al. (2008) correlateobserved braking behavior with HUD signal format(technology), age (driver) and time to stop line at lightchange (context).

Methodological orientations offer a second batch ofcomparisons between contributions. The relationshipbetween theory and data is the first one that comes tomind. The majority of contributions in this issue areessentially data-driven and theoretically agnostic. The

1Although the phrase ‘independent variable’ may seem the most

appropriate to some readers to designate what we call factors, we are

reluctant to use it here as it belongs to the experimental research tradition,

and using would make it sound like all DSS research is carried out within

this approach.

methodological approach they take to collect and analyzedata does not rely explicitly on a specific theoreticalframework. There are however two exceptions: Takayamaand Nass’s (2008) hypotheses are grounded in a theory ofcomputers as social actors, and the variables measured byRajaonah et al. (2008) are the ones that are related in theirtheoretical model (i.e. ACC use, trust, perceived workloadand perceived risk), inspired by Riley’s (1996) theory ofoperator reliance on automation.The nature of the collected data yields another type of

comparison. As mentioned earlier, DSS research can becarried out in environments that are more or less controlledby the researcher. The articles that appear in this issue leantowards the ‘‘controlled’’ end of the continuum, as all ofthem involve data collected in simulators. The simulatorsused to collect the data analyzed in these six articles rangefrom low-fidelity to high-fidelity simulators. For example,Lee et al.’s comparative study of several alternative designsof their MOVE system used a low-fidelity simulatorrunning on a desktop workstation equipped with twoscreens and a steering wheel. In contrast, Milleville-Pennel(2008), Rajaonah et al. (2008) and Caird et al. (2008) usedhigh-fidelity fixed-base simulators that provide a muchmore immersive experience to the participants. Addition-ally, Lee et al. (2008) report on a visual search study thatbelongs to the laboratory studies category outlined above,the most controlled type of data collection.The degree of control exerted on data collection has

direct consequences on the validity of the findings theysupport, which manifest themselves in the form of atradeoff between internal and external validity. Typically,there is a positive correlation between control and internalvalidity, i.e. controlling data collection reduces the prob-ability of plausible alternative explanations, since parasiticvariables are supposed to be held constant. On thecontrary, there is a negative correlation between controland external (ecological) validity, i.e. a more controlledenvironment produces data that are less likely to resemblethe phenomenon of interest as it happens in its naturalsetting, since the observed phenomenon is abstracted fromthe context it usually occurs in.Several factors may explain the fact that the majority of

research work related to DSS tends to draw on simulated,more controlled environments, and hence to favor internalover external validity. The first one of them is the lack ofmaturity of the technology involved in those studies,considering the potential risks of putting novel DSS to thetest on the road with actual vehicles. For example, exceptfor Rajaonah et al.’s contribution (which focuses on ACCuse), all of the articles in this issue report on the evaluationof prototypes or experimental concepts that would be toohazardous to be tested ‘‘in the wild’’ as of today. Anotherfactor is the need for results that can be generalized to thewhole population of drivers. Observation of drivers inthe real world usually relies on ethnographic methods thatare traditionally recognized as being better at producingthoroughly documented case studies than at generating

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findings that can readily be broadened to entire popula-tions. By contrast, laboratory and simulator studiestypically afford quantitative data analyses that produceresults evaluated in terms of statistical significance.However, although statistically significant results give theimpression of having a greater potential for generalization,this potential must always be considered with respect to theecological validity of the data that support the results.Ultimately, it appears that both paradigms struggle withsimilar difficulties with reference to the balance betweeninternal and external validity, but that one of them benefitsfrom more confidence in the world of DSS design. Finally,a last explanation may hold in the difficulty to translateethnographic descriptions into engineering specifications.Attempts to bridge this vocabulary gap exist (Boer et al.,2005), although they remain rare.

The last set of features relative to which we would like tosituate the articles in this issue is related to the relationship

between research and DSS design. In that context, thesearticles differ in terms of the types of results they areexpected to produce. Some of them (Rajaonah et al., 2008;Takayama and Nass, 2008) are aimed at studying aphenomenon related to driver behavior and cognition.Others explore design concepts to define new directions inDSS development (Li and Milgram, 2008; Milleville-Pennel, 2008) or test alternative prototypes to determinetheir respective merits (Caird et al., 2008; Lee et al., 2008).Another option (not represented in this issue) would be toaim at validating or evaluating existing systems.

Another way of positioning these articles with respect toDSS design is to consider the degree to which theimplementation of the DSS they focus on is currentlyrealistic, or the convertibility of their findings into designguidelines. On the one hand, contributions like those ofMilleville-Pennel (2008) and of Li and Milgram (2008)examine the utility of novel concepts to convey criticalinformation to the driver. Their direct applicability isreduced because their integration into vehicles stillrepresents a technical challenge: as of today, no system iscapable of tilting the visual field of the windshield. Still,such research work has the merit of thinking outside thebox, without limiting itself to what is currently technicallyachievable. On the other hand, contributions like those ofTakayama and Nass (2008) and of Lee et al. (2008) yieldclear design guidelines. Takayama and Nass highlight thefact that no matter if information provided to the drivercomes from an in-car agent or a remote system, designersmay have to present it as coming from within the vehicle.Lee et al.’s work on map generalization provides validateddesign principles related to the use of abstraction in maps.Incidentally, their article is the only one in the selection toexpand on the design process itself.

As we have tried to show in the preceding paragraphs,the research field of driver-centered design is diverse inmultiple ways and this diversity is probably its mainrichness. The exploration of novel ways to enhance thedriving experience, to make it safer and more enjoyable

greatly benefits from this variety in terms of theoreticalapproaches, methods and focus. The articles included inthis special issue represent a snapshot of what this diverselandscape looks like today.

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