Predicting the

Distraction of Car

Drivers by Cognitive

ModellingWhen reviewing the literature on accident research it becomes obvious that the distraction of car drivers has increased intensively during the last years and that it is a serious reason for many car crashes: drivers are not only faced with a more complex traffic environment, but at the same time the availability of in-vehicle information systems has in-creased, too. For sure, a major aim must be to gain a better understand-ing of the underlying distracters and to provide tools for designing both the traffic environment and the in-vehicle interfaces less demanding.

1 Introduction

Questions related to the design and theevaluation of human-machine interfaces are often examined within empirical us-ability tests. This procedure does not only afford an almost operational prototype,but it is also very time intensive and costdemanding. Within this work an alterna-tive approach to the “classic” usability study is presented, which may be partic-ularly helpful within an early period of product development. Its major objectiveis to evaluate the degree of distractioncaused by a secondary task while driving– without relying on empirical studies or on physical prototypes of the interface.

Its core component is a valid compu-tational model of a human car driver, that is able to “replace“ human subjects by simulated, fictive subjects (see Section2). To predict the degree of distraction caused by a secondary task, the interac-tion with this additional task is to bemodelled, too. While the driver model

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may be used again and again, the second-ary task is rather context specific and has to be modelled each time anew. For this reason, rapid-prototyping tools are needed, which facilitate cognitive model-ling in practice (see Section 3). As the ap-proach of cognitive modelling is rather new within the field of automotive inter-rrface design, both its advantages and dis-advantages shall be discussed at the end (see Section 4).

2 Modeling the Driving Task

It is obvious, that human subjects may only be “replaced” by a computational model, if it behaves very similar than ahuman being would do in the same situ-ation. What is needed is a good under-rrstanding of human information process-ing and a sound theoretical basis of how humans drive a car (see Section 2.1). Ascar drivers are by far not homogenous, it is also necessary to cope with individualdifferences and to consider various typesof driving (see Section 2.2).

2.1 Driver ModelWithin the last years a lot of studies have been dedicated to the cognitive model-ling of the driving task [1]. As particularly the work of Salvucci [2] has contributedto this field of research, the followingoverview focuses mainly on his driver model, Figure 1.

Within its current version the Salvuc-ci-Model simulates driving on a highway environment with multiple lanes and with medium density of traffic. It con-sists of three core components, namely (a) controlling the car, (b) monitoring the traffic environment, and (c) taking ap-propriate decisions:– control: For modelling the lateral and

the longitudinal control task the ap-proach reproduces human-like behav-vviour. Empirical studies suggest that human drivers guarantee a stable lat-tteral control of their vehicle by relying on the information of two points. For this reason, here too, a far point and anear point are distinguished. The far point is located at the rear end of a lead car and, if no lead car is available,the far point is identical with the van-ishing point (straight roadway) orwith the tangent point (curved road-

way) of the street, respectively. Be-sides, the information of a secondpoint is needed, which is locatedabout five meters in front of the egocar and which is called near point. Ina certain period of time the drivermodel switches its attention first tothe near point and then to far point,it encodes each visual angle and de-rives the amount of change comparedto the last cycle time. On the basis of the perceived difference an incremen-tal adaptation of the steering angle isrealized. This process is PI-controlledand it aims for achieving a stable nearand far point (proportional part),while keeping the vehicle in the cent-tter of the lane (integral part). For thelongitudinal control task the timegap to a lead car is assessed and com-pared to the value of the precedingcycle. The control law is designed torealize both a desired time gap and aconstant following behaviour, so thatthe driver model is also able to accel-erate and brake appropriately.

– monitor: To consider the forward aswell as the backward traffic and to ob-tain situation awareness, the Salvucci-Model switches its attention to theleft and the right lane with equalprobability. Whenever another vehi-cle is available its lane, direction, anddistance in relation to the ego car areencoded and stored in memory.

– decision: By referring to the informa-tion that is gained by the control andthe monitor task, tactical decisions are drawn; up to now this aspect islimited to the lane change behaviour.Thereby, a lane change is initiated by shifting both the near point and thefar point from the current lane to thedestination lane.

The three components – control, moni-tor, and decision – are designed as inde-pendent sub-tasks, whereby the first one

is accomplished with the highest priori-ty. Besides, all of these tasks are carriedout in a serial manner. Thus, for instance,the driver model encodes either the sur-rrrounding traffic on a certain lane, the near point or the far point – but none of these distinct spaces is perceived at thesame time. At a first glance this seems tobe a major drawback of the Salvucci-Mod-el. However, this limitation mimics a bot-tleneck which is typical for human infor-rrmation processing and thus, it ensures a good fit to the behaviour of “real” cardrivers. When driving on a straight road,for instance, the model does not producea mathematically optimal behaviour, but it performs rather human-like, Figure 2.

Besides, the Salvucci-Model distinguish-hhes by further characteristics that are rea-sonable from a psychological point of view. As it would be rather difficult to considerall cognitive processes, which are relevant

The Authors

Dr.-Ing. Barbara Deml is an assistant profes-

sor of Cognitive Ergo-

nomics at the Human

Factors Institute, Univer-

sität der Bundeswehr

München (Germany).

Figure 1: The driver model [2] was realized with the cognitive architecture ACT-R [3]

Hendrik Neumann is a research associate

at the Human Factors

Institute, Universität

der Bundeswehr

München (Germany).

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for such a complex task as driving, no “adhoc” modelling technique is chosen, but the driver model is implemented within acognitive architecture. A cognitive archi-tecture is on the one hand a comprehen-sive theory of human information process-ing and on the other hand it is a program-mmming environment with which certain as-pects of human behaviour may be de-scribed in the form of a computer pro-gramme. The Salvucci-Model was realized with a rather wide spread production sys-tem that is called “Adaptive Control of Thought – Rational” (ACT-R 6.0, [3]), and thus, the driver model is written in LISP.

2.2 Individual ModelsThe driver model as described above pro-vides a good starting point, but it is by nomeans complete. It does not consider fur-rrther traffic situations than driving on a highway (e.g. urban, rural road) and it does not account for any inter-individual variability between different types of cardrivers. However, there are several possi-bilities of adapting the driver model to meet the heterogeneity on our roads:– operational level: It has been shown

that changing the control parametersof the model accounts for differentsteering profiles [2]. For modellingmore complex constructs, such as a“comfortable” or a “sporting” drivingstyle, further adaptations in the con-trol function are needed [4].

– cognitive level: As ACT-R is a rather com-plex architecture it is not completely free of parameters and sometimes thedefault settings of some parameters are to be changed in order to meet individ-ual differences. This procedure requiresa strong theoretical grounding and itmust be applied very carefully. Such, for instance, varying the speed of cogni-iitive processing accounts for some ef-fffects that are due to the age, the amount of fatigue or the alcohol consume of drivers. Furthermore, it is also reasona-aable to adapt the content of the declara-tive memory (e.g. knowledge of vehicle behaviour depending on driver experi-ence) or to alter procedural strategies (e.g. moment of lane change depending on readiness of assuming risk). Finally,an extension of the architecture can be taken into account, too, in order to copewith emotional processes related todriver stress.

3 Modelling the Secondary Task

For predicting the amount of distractionthat is caused by a new interface, it is nec-essary to have both a model of the driving task and a cognitive model of the second-ary task. Providing a valid model of the driving task is mainly a scientific venture.In contrast to this, the secondary task is rather context sensitive, and thus, it is most often left to the practitioner to mod-el this activity. Taking into account thatthe here addressed practitioner works pri-marily on the design of user interfaces and not in the domain of cognitive model-ling, this may cause a problem.

To discuss this issue in more detail,two examples of secondary tasks arepicked out. Both tasks are related to thedomain of “visual search” and thus, both are supposed to interfere with the driv-vving task, which is also mainly visually demanding. The first instance refers to processing direction information on traf-fffic signs and it is modelled by “hand” (see Section 3.1); the second instance re-fers to selecting a target in a navigation system and here, a software tool is used to support the modelling procedure (see Section 3.2). Both approaches are dis-cussed against the background of their practical applicability.

3.1 Modeling by “Hand”In order to derive guidelines for the de-sign of direction signs an empirical study [5] was carried out at the Human Factors Institute. In a further work [6] it has beenshown that the times, which are needed to search and to recognize certain targets on direction signs, may also be predictedby an ACT-R model, Figure 3.

Just as the driver model this cogni-tive model is “handcrafted”. However, toproceed in a pragmatic way an already existing cognitive model of a visual search task [7] was taken as a startingpoint and it was adapted correspond-ingly. Against the background of indus-trial use the following three issues are of particular interest:– Development effort: What is needed is

a computational model that is able todrive a car, while at the same time processing directional information just as a human subject would do. It took two days to realize such a model. In contrast to this, planning and car-rrrying out the empirical study took at least two weeks and thus, the ap-proach of cognitive modelling is to be judged as efficient. However, it is to be mentioned that the cognitive model was developed by an experienced soft-ttware engineer.

Figure 2: The cognitive model shows similar driving skills than real human subjects in a racing track (reference: Halbrügge, M., Universität der Bundeswehr, Human Factors Institute)

Figure 3: For the perception of direction signs the driver model behaves similarly than real car drivers. The reaction times are longest when the target is not available (nv, for further interpretation see [5])

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– Expert knowledge: When workingwith a cognitive architecture the mod-eller is bound to a certain theoretical framework and, for sure, this easies cognitive modelling a lot. Neverthe-less, it is necessary to have some back-kkground knowledge in cognitive psy-chology. When modelling the accom-plishment of a secondary task while driving, it is particularly important toknow what conflicts may occur due to limited attentional resources.

– Transferability: In order to minimizethe effort of writing cognitive modelsby “hand” an already published model [7] was re-used that describes how a usersearches through a menu in a graphical user interface. Although this re-use ismuch easier than starting from scratch, the cognitive model had to be adaptedin some parts to meet the requirementsof reading and recognizing targets ondirection signs in road traffic.

It is to be summarized that writing cog-ggnitive models by “hand” offers a high de-gree of flexibility and provides a good understanding of the underlying cogni-tive processes. At the same time this ap-proach does not only afford background knowledge, but it constitutes also devel-opment effort. For this reason, other methods are to be discussed, too.

3.2 Modelling with Software ToolsTo facilitate cognitive modelling for non-experts a number of software tools have been suggested and their usefulness hasalready been demonstrated in some case studies (e.g. interference of car drivingand telephoning, [8], [9]). Empirical stud-ies have shown that the whole process of evaluation – from defining a prototypeto simulating a user interaction – took most often no longer than a few minutes [9]. To gain a better understanding of how a cognitive model is realized withsuch a tool, the use of CogTool [8] shall be demonstrated briefly, Figure 4.

While the above mentioned examplereferred to the visual search of direction signs, the question here is how to design menu navigation best. To answer such aquestion it is reasonable to realize various layouts and to compare them. When work-kking with CogTool [8] a “storyboard” of such a design option is to be created first. For this purpose sketches are loaded in the sys-tem that show different states of the inter-rr

face. These are used to specify interactive areas (e.g. buttons, text input) and by click-kking through the interactive sketches – justas a user would do – a task sequence is de-fined. As at the same time an ACT-R scriptis generated, this procedure is also called“modelling by demonstration”. The cogni-iitive model provides feedback on how long it takes until an experienced user wouldaccomplish the task. Besides, it is possible to visualize different steps of cognitive processing and to extend the setup by adriving simulation for predicting how theinterface would interfere with the drivingtask. It is to be summarized that the ap-proach is far less flexible and accuratethan a “handcrafted” model, but it is at the same time also much more intuitive.

4 Summary

Keeping busy with some kind of second-ary task while driving has become much more frequent within the last years. Theamount of distraction that is caused by such an additional interface is usually measured by usability studies. In addi-tion to that cognitive modelling consti-tutes a further assessment procedure that becomes more and more popular asit offers a number of advantages:– When “replacing” human subjects by

cognitive models, the amount of em-pirical user studies, which are notonly time consuming but also cost in-tensive, may be reduced.

– Just the same, less physical mock-upsare needed when developing new in-terfaces.

– Besides, simulated subjects do notonly allow for a better controllability,but unlike real subjects they may also drive faster than real time [9].

At the same time it is to be mentioned, thatthe approach of cognitive modelling willonly be accepted in practice, if valid driver models are provided and if software toolsare available, which allow for a flexible andintuitive modelling of secondary tasks. Asthere is much research on both of these as-pects, a major impact on cognitive model-llling is to be expected in the near future.

References[1] Cacciabue, C.: Modelling Driver Behaviour in Auto-

motive Environments. Berlin: Springer, 2007

[2] Salvucci, D.: Modeling driver behavior in a cognitive ar-rr

chitecture, Human Factors (2006), Nr. 48, S. 362-380

[3] Anderson, J. et al.: An Integrated Theory of the Mind,

Psychological Review (2004), Nr. 111, S. 1036-1060

[4] Deml, B. et al.: Ein Beitrag zur Prädiktion des

Fahrstils, In Fahrer im 21. Jahrhundert. In HMI und

Innenraum, VDI-Bericht (S. 47-59). Düsseldorf: VDI-

Verlag GmbH, 2004

[5] Halbrügge, M., Deml, B. et al.: ACT-TT CV: Kognitive Be-

nutzermodelle interagieren mit der Außenwelt, In M.

Grandt & A. Bauch (Hrsg.), Simulationsgestützte

Systemgestaltung (S. 313-331). Bonn: DGLR, 2007

[6] Färber, B. et al.: Aufnahme von Wegweisungsinfor-

mation im Straßenverkehr – AWewiS. In Berichte

der Bundesanstalt für Straßenwesen –

Fahrzeugtechnik, Nr. 979. Bremerhaven: Wirt-

schaftsverlag, 2007

[7] Byrne, M.: ACT-TT R/PM and menu selection: applying

a cognitive architecture to HCI, Int. Journal of Hu-

man-Computer-Studies (2001), Nr. 55, S. 41-84

[8] John, B. et al.: Integrating models and tools in the

context of driving and in-vehicle devices, ICMM

(2004), S. 130-135

[9] Salvucci, D. et al.: Distract-R: Rapid prototyping

and evaluation of in-vehicle interfaces, CHI (2005),

S. 581-589

Figure 4: When modelling with CogTool [8] a “storyboard” is created first and then possible interactions as well as task sequences are specified. The ACT-R model is generated auto-matically, whereby not only execution times but also cognitive processes may be visualized

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