a theoretical and methodological framework for studying and modelling drivers’ mental...

Safety Science 47 (2009) 1205–1221

Contents lists available at ScienceDirect

Safety Science

journal homepage: www.elsevier .com/locate /ssc i

A theoretical and methodological framework for studying and modellingdrivers’ mental representations

Thierry Bellet *, Béatrice Bailly-Asuni, Pierre Mayenobe, Aurélie BanetINRETS (LESCOT) – 25 Avenue F. Mitterrand, 69675 BRON cedex, France

a r t i c l e i n f o

Keywords:
Driver modellingMental representationCognitive simulationSituation awarenessOperative knowledgeDriving schemaLoop of controlExplicit and implicit cognition
0925-7535/$ - see front matter � 2009 Elsevier Ltd. Adoi:10.1016/j.ssci.2009.03.014

* Corresponding author. Tel.: +33 (0)4 72 14 24 57E-mail address: [email protected] (T. Bellet).

a b s t r a c t

This article provides a synthetic overview of the research programme carried out at INRETS-LESCOT overthe last 10 years, in view to studying and modelling the mental representations of car drivers. Theoret-ically, this research is in line with two complementary scientific traditions: Human Information Process-ing theories on the one hand, and theories of Operative Activity on the other. As discussed in Section 1 ofthe article, attention is given to the functional representations of drivers, constructed ‘‘by” and ‘‘for” theaction, such as they are implemented in a driving situation, and taking into account both implicit andexplicit dimensions. This aim directly impacts on the methodological approach implemented, in so faras it entails defining an ‘‘experimental continuum” ranging from naturalistic observations of the drivingactivity (on the open road), to setting up more controlled experimental protocols in order to permit in-depth, systematic and reproducible scientific investigations of drivers’ cognition. Section 2 of the articlepresents a synthetic view of these methods, while Section 3 presents several significant results obtainedwith them. Lastly, the final part of the article focuses on the computational formalism defined at INRETS-LESCOT (i.e. the driving schemas) designed to model driver knowledge and mental representations, anddeveloped in a COgnitive Simulation MOdel of the DRIVEr called COSMODRIVE.

� 2009 Elsevier Ltd. All rights reserved.

1. Introduction: theoretical background relating to the driver’smental representations

Although a routine task of everyday life, car driving is nonethe-less a complex activity that potentially involves every level of hu-man cognition. Indeed, driving a car requires that drivers (i) selectrelevant information from the environment, in accordance withtheir current goals and the driving task demands, (ii) understandthe immediate situation and anticipate its progression in the moreor less long term, (iii) take decisions in order to interact appropri-ately – via the vehicle – with the road environment and the otherroad users, and (iv) and manage their own resources (physical, per-ceptive and cognitive) to satisfy the time constraints of the activityinherent to the dynamic nature of the driving situation. The selec-tive dimension of information collection is especially important asdrivers cannot take in and process all the information available inthe road environment. As we shall demonstrate in this article, thisinformation is not selected haphazardly. It depends on the aims thedrivers pursue, their short-term intentions (i.e. tactical goals, suchas ‘‘turn left” at a crossroads) and long-term objectives (i.e. strate-gic goals, such as reaching their final destination within a giventime), the knowledge they possess (stemming from their previous

ll rights reserved.

; fax: +33 (0)4 72 37 68 37.

driving experience) and their attentional resources available at thisinstant. Information selection is the result of a complex processwhose keystone is the driver’s mental representation of the drivingsituation. Indeed, from their interaction with the road environ-ment, drivers build mental models of the event and objects thatsurround them (Johnson-Laird, 1983; Norman, 1983). These men-tal representations are circumstantial constructions (Richard,1990), formulated in Working Memory (WM) on the basis of per-ceived information on the one hand, and from activated permanentknowledge (stored in Long Term Memory; LTM) on the other(Fig. 1). More precisely, occurrent mental representations are cog-nitive emergences, produced through a matching process (i.e.instantiation) between LTM knowledge and reality. From thisstandpoint, understanding a situation means being able to repre-sent it (Richard, 1990), since representing it means assimilatingthis situation with previous knowledge, activated in the particularcontext of the moment.

Regarding these initial definitions, our approach is in line withthe traditional Human Information Processing theory underlyingcognitive psychology. Mental representations form the kernel ofcomplex sequences of cognitive processes, ranging from the per-ception of events to driving behaviours, through intermediatesteps of decision-making and activity planning (Rasmussen,1986). However, care is required to avoid taking an over-linearand sequential view of this processing string. Although the

mailto:[email protected]

http://www.sciencedirect.com/science/journal/09257535

http://www.elsevier.com/locate/ssci

Fig. 1. Elementary architecture of the human cognitive system.

1 In order for a human being to control a phenomenon, the brain must be able toform a reflection of it. The subjective reflection of phenomena in the form of feelings,perception and thoughts provides the brain with the information essential for actingefficiently on this phenomenon in order to achieve a specific aim (Leontiev, Lerner,Ochanine, 1961).

1206 T. Bellet et al. / Safety Science 47 (2009) 1205–1221

perception of an unexpected or critical event sometimes triggersthe processing sequence, it is more often the action in progressand/or the drivers’ intention (the aim they seek to attain in the cur-rent situation) that directs their perceptive exploration and infor-mation processing. More than a linear sequence of perceptive–cognitive processes, driver’s mental activities should be describedas an iterative ‘‘perception M action” cycle of regulation, organizedaround the mental representation of the driving situation. In thiscontrol loop, perception is constantly fuelled by actions which inturn constantly fuel perception. Our approach therefore followson from Operative Activity theories stemming from European ergo-nomics and Russian psychology. In this article, we aim to providean integrated view of these two different theoretical approaches,in the specific context of car driving. These theoretical differencesand mutual enhancements concern (i) the architecture of the hu-man cognitive system, (ii) the nature of the driver’s mental repre-sentations, and (iii) the role of these representations in theregulation process (control loop) of the driving situation.

1.1. Cognitive architecture: the central role of the Working Memory

At functional level, the Working Memory described in Fig. 1 stemsas much from the operative theory of memory formulated by Smir-nov (1996) and Zintchenko (1966), and then Bisseret (1970), as fromBaddeley’s Working Memory (WM) model (1986). For Smirnov(1966), memory is intimately linked to activity: man becomes awareof the world around him by acting on it, and transforming it. This ap-proach to human memory integrated in operative activity is at theorigin of Zinchenko’s concept of operational memory (1966). Opera-tional memory is defined by this author as a mnesic structure whosemain function is to serve the real needs of the activity. For Bisseret(1970), operational memory should be clearly distinguished fromLTM, since the information it encompasses focuses on the operator’scurrent goals. Thus it is a transitory rather than permanent memory.However, it should also be distinguished from Short Term Memory(the only mnesic structure able to temporarily store information inthe cognitive theories of the 1970s; e.g. Atkinson and Shiffrin,1968), in so far as the information it contains remains available foras long as it is useful to perform the activity in progress. Accordingto this view, operational memory far exceeds the storage and pro-cessing capacities of Baddeley’s WM. It appears to be a kind of LongTerm Working Memory, a definition 30 years in advance of the equiv-alent model formulated by Ericsson and Kintsch (1995). Conse-quently, the Working Memory of the cognitive architecturepresented in Fig. 1 must be understood from this functional interpre-tation of operational memory, i.e. as a structure that hosts activeknowledge instantiated through mental representations to servethe activity in progress (contrary to LTM which stores permanentknowledge in latent state).

1.2. Representations ‘‘for action”

On another level, the mental representations studied in thisarticle are not limited to the strict and reductionist definition of‘‘propositional representations” proposed by classical cognitivism(e.g. Fodor, 1975). Our approach is taken in the framework of Activ-ity theories.1 The driver is not a passive observer, but an actor of thedriving situation. He has control over it, and it is as an actor withintentions that he observes and represents the road environmentto himself. It is these kinds of operative models that form the coreof our premise. Operative representations are circumstantialconstructions formed in a specific context, and for specific ends(Richard, 1990). They are formulated ‘‘by” and ‘‘for” the action.Therefore they provide interiorised models of the task (Leplat, 1985,2004) constructed for the current activity, but which can be storedin LTM and reactivated later in new situations, for future perfor-mances of the same task. Thus the role of practical experience isdecisive in the genesis of these mental representations: it is ininteraction with reality that the subject forms and tests his represen-tations, in the same way as the latter govern the way in which heacts and regulates his action (Vergnaud, 1985). For this author, themain function of mental representations is precisely to conceptualisereality in order to act efficiently. In Section 4 we propose a specificformalism, called ‘‘driving schemas”, defined at LESCOT to providea computational model of drivers’ mental representations or, to bemore precise, to represent the operative knowledge underlying thesemental representations. However, the predominant idea we wish todevelop here is that these representations are not copies of objectivereality. They in fact diverge from it quite considerably. On the onehand, they only contain a tiny amount of the information availablein the environment: they focus in priority on useful information inorder to act efficiently in current traffic conditions, as a function ofthe goals pursued by the driver. On the other hand, they can alsoconvey much more information than that available in perceptiblereality (e.g. keeping in memory information perceived previouslybut henceforth hidden, formulating inferences of potential futureevents based on precursive clues, or anticipating the expected effectsof an action in progress). What is more, as illustrated by the works ofOchanine (1977) on Operative Images, these representations aresubjected to functional deformations (due to expertise effects), viathe accentuation of certain characteristics (salient and significant)relevant to the activity, and the non-acquisition of secondary detailswith respect to the current driver’s goal. For example, the driver’s

2 Michon (1985) distinguishes three levels in the driving task. The ‘‘strategic” levelcorresponds to planning and managing the itinerary, the ‘‘tactical” level correspondsto managing the current driving situation, and the ‘‘operational” level concernsimplementing the driving actions to control the vehicle.

T. Bellet et al. / Safety Science 47 (2009) 1205–1221 1207

mental model of an intersection can be very different according towhether s/he intend to turn left vs. turning right at the samecrossroads, or whether or not s/he have priority in carrying out thisoperation.

1.3. The role of mental representations in the regulation loop of thedriving activity

As with any dynamic environment, the road environment re-quires constant adaptation from the driver. From this standpoint,car driving has several similarities with other human activitiesrequiring the control of dynamic processes (Rasmussen, 1986).An important characteristic of a dynamic process is that the situa-tion will change even if the operator does nothing. The driving taskdoes not escape this constraint. Car driving can therefore be de-fined as an activity of regulating and maintaining the status ofthe dynamic process (i.e. the driving situation) within the limitsof acceptable and safe changes. As dynamic mental models of thecurrent situation, operative representations are useful for guidingthe performance of the driving activity in interaction with theenvironment. They therefore play a central role in information pro-cessing, which is part of the control loop governing the activity.Representations actively pilot the perceptive strategies for explor-ing the environment, as much as they guide behavioural activity, inorder to ensure the continual contextual adaptation and dynamicregulation of automobile driving. Furthermore, given the temporalimperative, the driver must not only react to events, s/he must alsoanticipate them. Therefore a central function of mental representa-tions is to support cognitive simulations (implicit or explicit) pro-viding expectations of future situational status. The driver uses acontinual updating process of occurrent representations as andwhen they carry out the driving activity, thereby ensuring theactivity permanence through time, with respect to the history ofpast events on the one hand, and future changes in the current sit-uation on the other. Lastly, car driving entails managing one’s ownresources to satisfy the demands of the road situation as well aspossible. One characteristic of human beings is their limits in termsof attentional resources. Therefore drivers have to manage theircognitive resources according to the short term (e.g. reacting with-in a given time to deal with the current situation, conforming tothe highway code) and long-term driving task demands (e.g. timeof arrival), while taking their driving experience into account toachieve the task successfully (i.e. what they know of their owncapacities and of the characteristics of their vehicle). Hence tempo-ral pressure impacts on activity regulation strategies. At perceptivelevel, this requires that the driver be highly selective in their searchfor information in order to limit collection and processing time. Onthe cognitive level, the need to act within a limited time deter-mines the levels of control and the nature of the mental processesimplemented (e.g. anticipation, decision, reasoning, planning).Drivers do not analyse all the behavioural alternatives availableto them rationally and systematically. They activate one of theirdriving schemas available in LTM, so they can approach the goalthey have set themselves as closely as possible, after which theyadapt this schema in real time in order to optimise their perfor-mance. In brief, driving a vehicle often entails making decisionsand acting in uncertainty as a function of the explicit and implicit‘‘awareness” one has of the ‘‘situation”.

1.4. From mental representations to ‘‘Situation Awareness”

For about 20 years, the notion of Situation(al) Awareness (SA)has given rise to much debate in the scientific community of ergo-nomics (e.g. the special issue of Human Factors of 1995, Stantonet al., 2001). The work of Mica Endsley constitutes one of the mainreferences on the issue. According to Endsley (1995), Situation

Awareness is defined as the perception of the elements in the envi-ronment within a volume of time and space, the comprehension oftheir meaning, and the projection of their status in the near future.In her theory of SA in dynamic situations, Endsley proposes a modelof human decision-making based on three different levels of SA: (i)the level of event perception, (ii) the level of interpretation of thesituation, and (iii) and the level of anticipation (projection throughtime of the future status of the situation). In her model, Endsley in-sists on the fact that the quality of SA can be affected by the quan-tity of cognitive resources available. This aspect is important fromboth the theoretical standpoint (meaning that SA stems at leastpartially from attentional processes) and that of practice (the needto take into account, when designing driving posts, the effectscaused by shared attention on SA). The main criticism that canbe levelled at Endsley’s theory stems from the sequential natureof information processing in her model (perception, then interpre-tation, then anticipation, then decision, then action), which is anover-simplified view of cognitive activity in a situation of dynamicregulation. From our point of view, these different steps require acircular vision and the introduction of ‘‘short-circuits” betweenthem. For example, the phases of situation interpretation (level 2of SA) and anticipation (level 3) generate expectations and informa-tion needs that have a direct impact on perception itself (level 1) atthe next ‘‘time step”. Moreover, it has been shown that in a dy-namic situation (e.g. Hoc and Amalberti, 1995), human beings of-ten make decisions in uncertainty (before SA has beencompletely formulated) or that certain actions play a decisive rolein situational analysis (validation ‘‘by” or ‘‘through” the action ofan initial SA, put forward as a simple hypothesis). This cognitivecircularity and the interlinked nature of processing levels, rein-forced by the anticipatory capacities of the human cognitive sys-tem, argue in favour of changes to Endsley’s model in order totake better account of the regulation activity in its dynamics. Inspite of this criticism, from which certain theoreticians of SA havedisassociated themselves by including their approach, as we didbeforehand, in Activity theory (Bedny and Meister, 1999), wenonetheless feel highly pertinent to place ‘‘Situation Awareness”(assimilated with an occurrent mental representation) at the veryheart of human activity analysis. As we shall see, this issue ofawareness is closely linked with the question of level of control.

1.5. Levels of awareness, levels of control

We have all undergone the experience of highly automateddriving, almost outside the field of one’s own awareness, whenconcerned by other worries, to the point of finding oneself in frontof one’s home without being aware of the route taken to get there(and then question oneself in a bout of a posteriori awareness: ‘‘myGod, how did I get here alive?”). Thus a great share of the car-driv-ing task, from selecting a familiar route (strategic component2) tohandling the controls (operational component), can be performedunder a low level of control and awareness. In this case we talk ofimplicit awareness of both the situation and the activity. On the con-trary, we can also all remember situations in which real-time drivingdecisions were judged as very complex, difficult, cognitively costlyand even sometimes critical, requiring all our attention in order tocarry them out successfully. When entering a crossroads by cuttinginto a flow of traffic with right of way, for example, or when we de-cide to overtake another vehicle on a narrow country road, we oftendo so after taking explicit and voluntary decisions, even if the rea-sons underlying them may be only partly conscious. In this case


we speak of explicit awareness of the situation and one’s intentions,decisions and actions. These two levels of awareness, implicit andexplicit, co-exist in the car-driving activity, as with most othereveryday human activities. This dichotomy is, moreover, well estab-lished in scientific literature, for example, in psychology, with thedistinction put forward by Scheinder and Shiffrin (1977) betweencontrolled processes, which require attention and which can only beperformed sequentially, and automatic processes, which can be per-formed in parallel without the least attentional effort. This is alsohighlighted by Piaget (1974), who distinguished between pre-re-flected acts on the one hand, and reflected thought on the other. How-ever, this distinction has also become classical in Ergonomics sincethe works of Rasmussen (1986), who distinguishes different levelsof activity control according to whether the behaviours imple-mented rely on (i) highly integrated sensorial-motor reflexes (skill-based behaviours), (ii) well mastered decision rules for managingfamiliar situations ( rule-based behaviours), or (iii) more abstractand generic knowledge that is activated in new situations, for whichthe operator cannot call on sufficient prior experience (Knowledge-based behaviours). Likewise in the field of Neurosciences, with Koch’szombie agents (2004), bringing to mind somnambulism and animalcognition (Proust (2003) speaks of proto-representations to qualifythis level of implicit awareness), in contrast with decisional, voli-tional acts, oriented towards reaching the subject’s explicit aim.Lastly, the field of Artificial Intelligence can be mentioned, with,for example, Smolensky (1989) distinction between levels of sub-symbolic (neuro-mimetic) vs. symbolic computation. Obviously, everycognitive sciences sub-specialities tend to converge in accepting thatdifferent regulation modes of activity exist, as do by consequenceseveral levels of awareness.

Regarding the operational functioning of the human cognitivesystem in a car-driving situation, a large share of the driver’s men-tal activity relies on heavily integrated and automated empiricalknow-how. Even when taking a deliberate tactical decision, suchas overtaking, a large part of implementing this decision will bedone by way of skill-based behaviours (e.g. pressing the accelera-tor, turning the steering wheel left to change lanes), partly escap-ing conscious control, but nonetheless relying on an implicitform of awareness and activity monitoring to guarantee that thegoal defined explicitly is reached. Thus the control levels of thedriving activity are embedded, as much for that which relies on

Fig. 2. Levels of awareness an

the process of understanding the road environment (situationalawareness), as that which relies on decision-making, planningand implementing driving behaviours via the vehicle. In veryfamiliar situations, driving mainly relies on an automatic controlmode requiring a low quantity of attentional resources, and thusis essentially based on an implicit awareness of the situation. Thisautomatic control loop of the activity (Fig. 2) is inaccessible tothe driver’s explicit awareness, except the emergent part (particu-larly, the tactical goal) that can be subject to decisional, intentionaland reflexive cognitive processing. However, if the situation sud-denly becomes abnormal or critical, or if one drifts too far fromthe limits of validity framing the routine regulation process (forexample, when an action does not bring about the expected ef-fects), then the highest levels of awareness are alerted. The water-line of the ‘‘iceberg of awareness” lowers: the driver’s attention isfocused on the problem to be solved, thus the activity is taken overby explicit control. To all intents and purposes it is clear that impli-cit awareness and explicit awareness cannot be seen as two sepa-rate entities. These two levels of awareness support and areembedded in each other. In reality, they are two sides of the samecoin: one cannot exist without the other. If a coin is placed on thetable so that only one side is visible, this does not mean that theother side has disappeared. It is still there, at least in latent state,as a base for the visible side. As a function of the driver’s experi-ence and skill, or according to the familiarity of the situation, bycontrast with a new or critical situation, his/her awareness willalternate between the more dominantly explicit, decisional andattentional level of cognition vs. implicit awareness in which theroutine and automatic processes dominate.

1.6. Explicit vs. implicit mental representations: from ‘‘emergence” to‘‘immergence”

A central process is at work in this dialogue between the differ-ent levels of situational awareness and activity control: the ‘‘emer-gence” process. This process can occur on two time scales. In theshort term, i.e. the temporality of the driving situation itself, it cor-responds to the irruption (more or less spontaneous) in the field ofexplicit awareness of information previously not perceived or onlytaken into account implicitly. In the long term, i.e. in the temporal-ity of permanent knowledge acquisition, emergence is involved in

d activity control loops.

Fig. 3. Control loops and learning loops.


the driver’s elicitation strategies, whether for learning by intro-spection or self-evaluation of their own competences (i.e. Piaget’sreflected thought), transmission of one’s competences to anotherdriver (how to negotiate this bend well), or justify one’s acts (ex-plain my behaviour to a policeman who has just stopped me!). Insymmetry with the notion of emergence of conscious thoughtand conceptual representations, we wish to add the notion of‘‘immergence” of this explicit thought to operative know-howand implicit awareness. This is to take into account the embeddingprocess of decisional cognition in operational activity, and todescribe the sedimentation phenomenon of explicit thought inimplicit knowledge, i.e. its operative embodiment in deep-cogni-tion (we could also speak with Anderson (1993) of compilation,or the proceduralisation of knowledge into know-how). The cyclicnature of this emergence vs. immergence process makes it possibleto unify these two temporalities (short term and long term): to-day’s emergences are the result of past immergences, and vice-versa. One is the echo of the other, on nonetheless different levelsof awareness (Fig. 3).

All drivers have sometimes been surprised and frightened whiledriving, when suddenly realising that their own situational aware-ness did not match objective reality. Either they did not take intoaccount an event correctly, or the knowledge activated turnedout to be wrong. From the phenomenological standpoint, the sur-prise effect is all the greater when one was initially convinced ofbeing in control of a familiar situation, by implementing routineregulation procedures and by relying on one’s implicit awarenessof events. A feeling of fear and stress often accompanies this pro-cess of emergence in the field of explicit awareness. Without beingable to tell exactly whether the emotion precedes or succeeds thisexplicit awareness, it probably plays a central role in fixing thistraumatic situation in episodic memory (Tulving, 1983), makinga posteriori analyses possible for the purposes of learning, in par-ticular. Thus in symmetry with the activity control loops (betweenoccurrent representations and the road environment), it is advis-able to add a second regulation loop between the representationsand knowledge stored in LTM, to take into account both the recov-ery process (from voluntary recall to spontaneous activation) and

the learning process (from active and intentional learning to effort-less implicit encoding). This second dual-loop refers directly to thedistinction made by Graf and Schacter (1985) between implicitmemory and explicit memory.

Lastly, to this relation of emergence and immergence of mentalrepresentations (from the implicit to the explicit, and vice-versa), itappears necessary to add a third dimension: reflexivity (i.e. aware-ness of being aware). This meta-level of ‘‘reflexive awareness”integrates two main aspects: ‘‘behaviour conceptualisation” and‘‘judgement of values”. The issue of conceptualisation has beenstudied in child development by Piaget (1974), and in adults byVermersch (1994), in the light of the ‘‘prise de conscience” mecha-nism (i.e. becoming aware) and the elicitation of operative knowl-edge of one’s own actions. According to these authors, this processis based on different steps ranging from ‘‘pre-reflected” acts to ‘‘re-flected thought” (reflexive and conceptual meta-knowledge allow-ing understanding of one’s own activity as an object of knowledge).However, it is also advisable to incorporate a value judgementdimension regarding the situation with this reflexive level, aboutoneself or one’s acts. This question is more familiar to the field ofsocial psychology (e.g. through attitude and social representationconcepts) than that of cognitive psychology. In the context of cardriving, this dimension of value judgement has been studied inparticular from the angles of risk assessment and/or drivers’ atti-tude in relation to risk-taking, for the purposes of deliberate sensa-tion-seeking and violation of the Highway Code. Moreover, theconceptualisation of operative activity and value judgements arenot independent. Indeed, implicit representations cannot be theobject judgements without a certain amount of prior conceptuali-sation in the form of explicit, reflexive and conceptual thinking. Onthe other hand, the result of reflexive judgements (e.g. I assess therelevance of an action, its legitimacy, efficiency, or soundnessregarding the other), once linked to explicit situational awareness,is liable to be durably integrated in the driver’s implicit awarenessthrough the immergence process of this representation in the formof implicit knowledge incorporating conformity with these meta-constraints. Road safety campaigns are partially based on this idea:the explicit denunciation of a specific behaviour linked with delib-


erately violent images, in view to durably marking drivers’ implicitawareness with a negative impression.

2. Methodology: an ‘‘experimental continuum to carry out ascientific investigation and model the mental representationsof car drivers

In this part, we shall provide a synthetic view of different meth-ods developed at LESCOT to carry out scientific investigations of cardrivers’ mental representations, taking account of their naturalcomplexity and their different cognitive dimensions (explicit andimplicit). This ambition is not without methodological conse-quences, in so far as the aim is to define an experimental contin-uum that stretches from the most natural and ecologicalobservations of the driving activity (on the open road), to settingup more controlled experimental protocols for carrying out sys-tematic and reproducible investigations of drivers’ cognition. It ismore particularly this ‘‘experimental continuity” that we seek toillustrate here, by successively dealing with three complementarymethods: (i) on-site naturalistic observations, (ii) on-board obser-vations of the driving activity by using an instrumented car, and(iii) in-lab experiments focused on situational awareness and riskassessment measurement. Each of these methods has its own lim-itations that we take care to emphasise, and which permit onlypartial understanding of the driver’s mental representations. Thusthe main aim is to highlight the synergy between these methodsand offer an overview before presenting in the following sectionseveral significant results obtained by implementing each of themso that they shed light on and enhance each other.

2.1. Naturalistic observations on a real site

This method relies on systematic observations of the activity ofdrivers using a specific road infrastructure. This type of observationcan call for instruments (e.g. radar) or require video recordings, butit can also make use of direct visual observations by a human ob-server. In the latter case the observer uses coding grids to qualifythe situation in real time, from his position of external observer.The main advantage of this approach is the ecological validity ofdata collected (if the observer and the equipment they use are keptdiscreet): the drivers observed pursue their own goals for theirown reasons, without having to care about anything else but driv-ing. Thus their natural activity was observed, free of any scientificimpediment. Nonetheless, the main drawback of this approachstems from the observer’s external position. Located in the infra-structure, s/he observes the vehicle behaviours more than the dri-ver’s activity, and only for a short period of time (when the vehicleis in the observation site). These are thus macroscopic observationsthat permit studying localised behaviours and provide a wealth ofinformation on driving strategies and multi-user interactions.However, there are often imprecise when focusing on driver cogni-tion. To achieve this, there is little choice but to ride in the vehiclebeing driven.

2.2. On-board observations of drivers’ activity using an instrumentedvehicle

This method consists in sitting next to the drivers and askingthem to drive along a specific itinerary. The more or less ecologicalnature of this itinerary can vary according to the experimentalobjectives and the type of variables to be measured and controlled(from driving on a test track to driving on a freely chosen itinerary).Regarding the results presented further on in this article, the obser-vations were performed on the open road, under real traffic condi-tions and on an urban route imposed on all the participants. The

vehicle used was a standard model available on the market (a Cit-roën ZX for the oldest studies, a Renault Scénic for the most recentstudies), but equipped with a large number of sensors to automat-ically record the drivers’ activity (e.g. their action on the pedals,steering wheel, turn signal lights, speed) and external traffic condi-tions (e.g. video films of front and rear road scenes). In addition, thedrivers were filmed in order to analyse their visual strategies andpostures, and their speech were recorded by a microphone. On-board observations were completed with an ‘‘interview of elicita-tion” (Vermersch, 1994). This interview was performed immedi-ately after the driving phase by using the video film as back-up.The elicitation interview and self-confrontation is a method thatprovides a great deal of information for the scientific analysis ofmental representations. The drivers were asked to view theirown driving activity, then explain it in answer to the questionsasked by the investigator. The advantage of using self-confronta-tion is that it provides the possibility of obtaining data not directlyobservable (e.g. the way the driver has understood a situation, esti-mated a risk, taken a specific decision). Although this interview isperformed after the driving phase, the drivers find it easy to men-tally re-project themselves in the situation as they had experiencedit at the moment it happened. What is more, it is far better to col-lect these verbalisations a posteriori, rather than ask the drivers tocomment on their activity ‘‘aloud” while driving: behaviour elicita-tion is an activity ‘‘in itself” that may disturb or modify the drivingtask. Several results obtained by using this approach will be pre-sented later in this article, following the observations performedon-site. The main drawback of this method is the difficulty ofreproducing the same driving situations for different drivers, atleast when focusing on driving under natural traffic conditions.Consequently it is also necessary to use laboratory protocols andmeasurement methods, especially when focusing on the cognitiveactivity of drivers in view to performing inter-populationcomparisons.

2.3. Measurement tools for the systematic and controlled investigationof representations

In view to performing an in-depth investigation of drivers’ men-tal representations, we have developed specific in-lab methodsbased on the presentation of video films of road scenes (filmedfrom the driver’s post). Confronting drivers with filmed sequencespermits interrupting situations at any time and recording theiropinions in different ways (e.g. verbalisations, drawing, filling inmeasurement scales), which is not possible at the steering wheel,especially under critical driving conditions. It is also possible toconfront different driver populations with exactly the same situa-tions, or, on the contrary, assess the impact of a specific modifica-tion of a video sequence for a given population of drivers. Althoughthe absence of interaction represents a limitation in comparison toa driving simulator, this limitation is counterbalanced by the real-ism of the road scenes and the strength of immersion provided byvideo films, by contrast with virtual images. This is especiallyappreciable when focusing on the perceptive and cognitive com-mitment phases (like event perception or road scene comprehen-sion) prior to the action.

The first in-lab method presented here is the ICARE protocol(InteraCtive tool for Assessing drivers’ Situation awaREness), dedi-cated to analysing drivers’ Situation Awareness (Bailly, 2004; Baillyet al., 2003). This protocol is based on the presentation of videofilms of suddenly interrupted road scenes. The last image of the se-quence, including modifications (i.e. addition or deletion of oneelement), was then shown to the participants who were instructedto indicate the modification(s) detected. In its most advanced ver-sion (including a Visual Feed-Back tool based on a 3D model under-lying the last image), ICARE also allows the participants to modify


the road scene directly, in order to generate a 3D view of their ownmental representation of the driving situation. This then makes itpossible to compare drivers’ representations objectively and ana-lyse SA errors precisely in their spatial and temporal dimensions.

The second experimental protocol dealt with here is the CRITICtool (Common RIsk awareness measurement meThod for Inter-popu-lation Comparisons), dedicated to the analysis and measurementof risk awareness (Banet and Bellet, 2008). The aim here is to studythe way in which drivers assess the critical nature of a driving sit-uation with a more or less long-term risk of collision. In terms ofcognition, this Risk Awareness phase is the starting point of any cor-rective action taken by the driver. The overriding principle of thisprotocol is to present the participants video sequences of ‘‘normal”road scenes that suddenly become ‘‘critical”. The experimental taskfirst consists in interrupting the sequences immediately they arejudged critical. Then, at the end of each sequence, the participantsmust estimate the criticality of the whole situation by using a Lik-ert scale (non-graduated) and express a value judgement on thissituation by using an Osgood’s semantic differential scale. This differ-ential scale is composed of 16 antonyms, four antonyms perdimension measured: (i) the objective and descriptive characteris-tics of the situation (e.g. simple vs. complex driving condition), (ii)its predictability (e.g. rare vs. frequent situation), (iii) the feeling ofinvolvement and control (e.g. the feeling of being responsible ornot for the criticality of the situation, or being capable vs. incapableof controlling it), and (iv) and the emotional feeling (e.g. impres-sion of danger vs. safety). It should be noted that the video se-quences used in this protocol came from a previous experiment(on-board naturalistic observations of ten experienced driversactivity on the open road, including collision avoidance situations)performed on an instrumented vehicle (Bellet, 2006), thus illustrat-ing our intention to ensure an experimental continuum betweenour different studies.

3. A few significant results

This section will be devoted to the synthetic presentation of afew significant results obtained at LESCOT regarding drivers’mental representations. It above all aims at showing the synergy

Fig. 4. The main left-turn (LT) stra

between the studies and methods, respectively, formulated to shedlight on particular facets of mental representations. If interested,readers may refer to other publications for specific and more de-tailed result presentations.

3.1. Analysis of strategies and representations in left-turn (LT)situations

The first series of results presented here is based on naturalisticobservations in the urban environment. It deals with the analysisof left-turn (LT) strategies at a traffic light controlled crossroads lo-cated close to INRETS (the ‘‘La Poudrette” crossroads). More than400 LTs were observed at this site (Bellet, 1998), including 307 inthe presence of oncoming traffic (only these cases are consideredhere). The main variables taken into account during observationwere: traffic conditions (e.g. occurrences and positions of vehiclestravelling in the opposite direction), respective driving paths andvehicle speeds variations, temporary stopping positions (waiting)at the crossroads, and the colour of the traffic lights. These obser-vations permitted distinguishing six main different LT strategies(Fig. 4): Direct passages (34.4% of LTs observed in the presence ofoncoming traffic) correspond to cases where at least one of thevehicles in interaction performs its LT without stopping. The possi-bility of making a direct passage is heavily dependent on trafficconditions. Nonetheless, some direct passages resulted from amore deliberate intention of the driver. Thus it is possible to speakof strategy in the true meaning to qualify this volitional attitude, incontrast with other direct passages that result more from a con-junction of favourable circumstances (20.8% of cases observed).Two volitional direct passage strategies were recorded: an oppor-tunist strategy and a dynamic regulation strategy. The opportuniststrategy (5%) aimed at beating the other drivers through speed(e.g. when the traffic lights changed from red to green, makingan LT before the oncoming vehicles could start). The dynamic reg-ulation strategy (8.6%) was very different. It was based on temporalmanagement (loss of time) of driving path conflicts. Thus somedrivers decided to let their vehicles drift slowly immediately onentering the crossroads. This allowed them to wait sufficiently longto ensure the total (low) flow of oncoming traffic. Therefore theysucceeded in making an LT without intermediate stops.

tegies with oncoming traffic.


Strategies based on intermediate waiting (65.6% of cases ob-served) are implemented when the driving path of the oncomingvehicles prohibits passing through the crossroads directly, therebyimposing intermediate waiting. It was rare that this form of wait-ing came to an end before the flow of oncoming traffic had stopped.However, this could occur when oncoming vehicles are still farfrom the lights (more than 50 m), or show their intention to turnby indicating. Different waiting strategies were observed at thissite and they can be distinguished according to the waiting posi-tion taken by the vehicle before carrying out an LT. Three distinctwaiting areas were observed, corresponding to three different LTstrategies. The 1/3 waiting position strategy (33.6%) consists inentering the crossroads then stopping at the level of the left laneof the perpendicular axis. The 2/3 waiting position strategy(23.4%) is comparable in every way with the previous one, thoughthe waiting area is located at a much more advanced position atthe crossroads (always beyond the middle of the crossroads),thereby allowing oncoming drivers the possibility to get roundthe vehicle. This strategy corresponds to the instructions of theHighway Code. Lastly, the waiting at entrance strategy (8.6%) con-sists in stopping at the entrance to the crossroads. This leavesthe initiative of choosing LT strategies to the oncoming vehicles.

Our observations show that this strategy is less frequent thanthe other LT strategies. However, nearly a quarter of 1/3 waitingpositions start from waiting at the entrance, without the vehiclecoming to a complete standstill: drivers progress on the path veryslowly, sometimes leaving the flow of oncoming traffic to come toan end. At the end of this slow drift, either the driver makes an LT,or they stop. Thus there is a continuum between certain strategies.Although certain drivers opt unambiguously for a determinedstrategy upon entering the crossroads, others prefer to use a formof dynamic regulation, only adopting a definitive strategy when thisresource has been exhausted, thereby illustrating the possibilitiesof adapting the activity in real time. In the second experimentalphase of this study, observations were performed on-board aninstrumented vehicle. Seven experienced drivers (three men andfour women; average age 40 years old) participated in this experi-ment. The experimental task consisted in driving on an urban routeand taking the La Poudrette crossroads several times (Bellet, 1998).Among the data collected (Fig. 4), 16 cases of LT with oncomingtraffic were observed (50% direct passages, 38% 1/3 waiting positions,6% waiting at entrance, and 6% of 2/3 waiting positions), thus repre-senting all the LT strategies observed beforehand on the site(naturalistic data).

At the end of the driving phase, filmed in full from the drivingpost (Fig. 5), the drivers were invited to comment on their activityby following the self-confrontation method: they visualised the

Fig. 5. On-board video collected.

video film of their LT activity in association with their behaviouralcurves, then they were invited to elicit their behaviours, theirunderstanding of the situation, their decision-making processes,and the knowledge they used to take them. Methodologically,interview of elicitation (Vermersch, 1994; cf. 2.2) allows the exper-imenter to jointly analyse drivers’ explicit awareness and implicitbehaviours implemented while driving. Then, similarly to Ocha-nine’s method implemented for operative image study (cf. 1.2),the participants had to make a drawing of the crossroads, afterwhich they commented on the drawing in order to describe theirusual LT strategies at an urban crossroads.

The main result of these joint-experiments (including naturalis-tic observations, on-board data collection, drivers’ interviews ofelicitation, and operative image analysis), was to show that thedrivers used a functionally structured mental model of the roadspace (Bellet, 1998) in order to progress in their environment:the road infrastructure is broken down into different areas towhich events are associated (real or potential occurrences) and/or actions implemented, according to these events. The ‘‘drivingschema of LT at an Urban Crossroads” shown in Fig. 6 presents thisfunctional breakdown. On the formal level, this driving schema isbroken down into (i) a tactical Goal (i.e. left turn), (ii) a sequenceof States, and (iii) a set of Zones. Two types of zone are distin-guished: Driving Zones (Zi) that correspond to the driving path ofthe vehicle as it progresses through the crossroads, and the Percep-tive Exploration Zones (exi) in which the driver seeks information(e.g. potential events liable to occur). Each driving zone is linkedto Actions to be implemented (e.g. braking or accelerating, in viewto reach a given state at the end of the zone), the Conditions of per-forming these actions, and the perceptive exploration zones thatpermit checking these conditions (e.g. colour of traffic lights, pres-ence of other users). A State is characterised by the vehicle’s posi-tion and speed. Three types of State are shown in Fig. 6: the InitialState (vehicle position and speed when approaching the cross-roads), the Final State (speed and position on leaving the cross-roads, constituting the tactical goal of the LT activity), and a setof Intermediate States, matching the potential waiting positions ob-served during our naturalistic observations. The different se-quences of the driving zones make up the Driving Paths thatprogress from the initial state to the final one (i.e. achievementof the tactical goal). In the LT driving schema below, two differentdriving paths are possible: Z1, Z2, Z3a1, Z3a2, Z4 or Z1, Z2, Z3b1,Z3b2, Z4. It should be noted that the final state of a given drivingzone is also the initial state of the following driving zone, therebyensuring the continuity of the driving path. In the light of the driv-ers’ activity observed at the La Poudrette crossroads, the deploy-ment of this driving schema in order to make an LT at acrossroads proceeds as follows: on approaching (Z1 driving zone),the driver looks to see whether the traffic lights are green in ex2. Ifthis is so, they enter Z2. Otherwise, they stop at the end of zone Z1,then wait until the light changes to green. Once entered in Z2, thedriver (after having generally explored Z4 to calculate their drivingpath) looks to see whether there are oncoming vehicles (ex3, ex4,ex5), or further (ex6) if they are moving fast. Otherwise (or if thesevehicles are performing only LT or RT), the driver can then enterZ3a1, then cross the opposing lane (Z3a2) without waiting (i.e. di-rect passage strategy).

In the opposite case, several strategies are possible. Accordingto the first case (waiting at the entrance), they stop at the end ofzone Z2, for enough time to allow the oncoming vehicles to enterthe crossroads. According to the second (1/3 waiting position) orthe third case (2/3 waiting position), they enter Z3a1 or Z3b1, be-fore waiting at the end of the zone. The choice between these dif-ferent strategies can vary according to the profile of the drivers,some of them having a preference for one of these strategies. Thischoice is also greatly influenced by the possible presence of other

Fig. 6. An example of the ‘‘driving schema”: left turn at an urban crossroads.


vehicles in the driving zones (e.g. car following situations), orcoming from the opposite direction. As soon as they are able(e.g. at the end of the oncoming vehicles flow, or after the identifi-cation of a gap in this flow), the driver then enters Z3a2 and Z3b2,respectively. Before entering Z4, they check that there is nopedestrian walking across the zebra crossing (ex7). If this is notthe case, they continue driving in zone Z4. Otherwise, they stopat the end of zone Z3a2, or Z3b2, allow the pedestrian(s) to cross,then continue driving in zone Z4 (exit of the crossroads). In the lastpart of the article, we present the computer model of the drivingschemas developed in the framework of the COSMODRIVE modelin order to perform the cognitive simulation of drivers’ mentalrepresentations.

Fig. 7. ICARE protocol

3.2. ‘‘Situation Awareness”: effects of driving experience, driver ageand attention sharing

The second series of results presented here focuses on the anal-ysis and measurement of Situational Awareness by using the ICAREprotocol (Bailly, 2004; Bailly et al., 2003). It should be recalled thatthis method is based on showing short video sequences that aresuddenly interrupted (cf. 2.3). The last image of the sequence,modified beforehand (deletion or addition of an element of thescene), is then shown to the participant (Fig. 7).

They must then indicate whether this image has been modifiedor not, and specify the type of modification detected. It should bementioned that all the experimental modifications of ICARE

(sequence no 24).

Table 1Percentages of correct detections of modifications.

Populations (groupsof 10–20 subjects)

In single taskcondition (%)

In double taskcondition (%)

SignificativityST/DT

Experienced drivers(20–40 years old)

74.9 61.5 S (p = 0.005)

Novice drivers (18–30 years old) 58.5 42.7 S (p = 0.005)Elderly drivers (+65 years old) 50.3 44.5 NS

Fig. 8. Percentage of right detection, according to the distance of the modification.


sequences concern major elements from the standpoint of car driv-ing. Thus the fact of not detecting a modification means that thedriver’s situational awareness, at least at this moment, was insuf-ficient to ensure their safety (e.g. non-awareness of a critical event,erroneous interpretation of priority rules at a road junction). Theprotocol can be applied by performing a simple driving task (i.e.ST conditions), or in parallel with a metal calculation task (i.e. DualTask conditions: DT), in order to measure the effects of dividedattention on the content of the mental representation of the situa-tion and appreciate the respective weights of automatic vs. atten-tional processes in SA construction. The results obtained with thisprotocol (Table 1) shown statistical significant differences for mostof the variation sources considered. It can therefore be seen thatdriver performance relating to detecting modifications dependson:

– Driving experience: experienced drivers achieved the bestperformance out of the populations considered (75% correctdetections of the road scene modifications).

– Driver age: when carrying out a simple task, the perfor-mance of elderly drivers was 25% lower than that of experi-enced drivers, even if experience permits partially offsettingthe negative age effect (less difference in comparison to nov-ice drivers).

– The attentional resources available: having a DT whilewatching the videos significantly degraded the participants’performance. This effect is proportionally more importantfor novices (fall of 15.8 points out of 58.8, i.e. a variation of27% in DT conditions) than for experienced drivers (fall of18%). But the DT effect it is not significant for the elderlydrivers.

These global results show that situational awareness dependson both automatic and attentional processes, in proportions corre-sponding to the differences in performance observed between STvs. DT conditions. Furthermore, although the negative age effects(which lead to a reduction of cognitive capacities in driving situa-tions) can be seen clearly on the content of mental representationsunder ST conditions, driving experience nonetheless allows elderlydrivers to offset these negative effects, more particularly in DTconditions (not any significant impact of divided attention forthe elderly drivers). Concerning novice drivers, the mental repre-sentation elaboration requires far more attentional resources. Un-der the combined effect of the expertise on the one hand, whichpermits better knowledge of where to seek relevant information,and the cognitive processes automation, on the other hand, drivingexperience plays a decisive role in SA quality. In complement to theprevious results focused on the perception and the comprehensionlevels of SA (i.e. level 1 and 2 of Endsley’s model, cf. 4.l), data col-lected with ICARE method also provides interesting results with re-gard to the anticipative dimension of the drivers’ SA (i.e. level 3).An aspect of this question is more precisely assessed via ICARE,by considering the detection performance according to the dis-tance of the modified component. Four distances of modificationwere tested: 0–15 m (nearby zone), 15–25 m, 25–50 m, and beyond50 m (far zone). As the speed of the car used during the video film

collection was of 50 km/h, these four zones correspond, respec-tively, to 0–1.1 s, 1–1.8 s, 1.8–3.6 s, and more than 3.6 s. Fig. 8 pre-sents the detection performances for each zone, according to thedrivers’ experience and the Dual Task effect. In Simple Task condi-tions, experienced drivers (Exp.) obtained better results than nov-ices (Nov.) concerning modifications detection into the nearbyzone (81.3% vs. 60%), and the 15–25 m zone (87.1% vs. 67.1%). Be-yond 25 m, the differences are not statistically significant. For theDT conditions, differences between Experienced and Novices aresignificant for 3 of the 4 zones. Performances are, respectively, of67.5% (Exp.) vs. 51.3% (Nov.) for the nearby zone (0–15 m), 67.1%vs. 42.9% for the 15–25 m zone, and 70% vs. 41.4% for the 25–50 m zone. Not any significant effect was found for the far zone.

If we now consider intra-populations differences in relationwith DT, a significant negative effect occurs in the nearby zone(81.3% vs. 67.5%) and the 15–25 m zone (87.1% vs. 67.1%) concern-ing Experienced drivers, whereas for Novices DT impacts mainlythe 15–25 m zone (67.1% vs. 42.9%), and the 25–50 m zone(64.3% vs. 41.4%). For all the drivers, not any significant differencewas found beyond 50 m, probably because they pay less attentionto this far area (in both ST and DT conditions). The main other rel-evant Non-Significant effects of the DT concern: (i) only novicedrivers for the 0–15 m zone, and (ii) only experienced drivers forthe 25-50 m zone. To interpret these two last results, it is necessaryto precise the DT effective impact in ICARE protocol. Indeed, the DTimplemented (i.e. mental arithmetic) considerably reduces thequantity of cognitive resources available for the driving scene anal-ysis, but not totally. Consequently, participants can allocate theresidual attentional resources to the pieces of information and/orareas of the road scene they consider as the most important. Interms of ICARE detection performances, it means that the DT im-pact will be inversely proportional to the level of relevance ofinformation and/or areas (i.e. the residual attention will be allo-cated in priority by the driver to his/her main area of interest).Thus, a Non-Significant effect of the DT indicates a high level ofinterest of this road environment section. For Experienced drivers(Fig. 8), DT does not affect the performance for the 25–50 m zone,whereas for novices, DT has no significant effect for the nearbyzone (0–15 m). This difference indicates that novices and experi-enced drivers have not the same ‘‘focal point” (Figs. 8 and 9) interms of both residual resources allocation and visual scanning pri-ority of the road environment. For novice drivers, the residualattention is firstly allocated to the nearby environment of the car(0–15 m; i.e. from 0 to 1.1 s). This fact is due to the lack of drivingexperience, requiring an attentional monitoring of the short termdriving activity. Consequently, the 0–15 m area is the main focalpoint of their attention. On the Fig. 9, it corresponds to the contin-uous circle focused on the immediate environment of the car. For

Fig. 9. Drivers ‘‘focal points” according to the driving experience (dotted circle forexperienced drivers, continuous circle for novices).


experienced drivers, driving experience allow them to automati-cally manage the driving activity in the nearby area. Consequently,their residual attention is mainly allocated to the 25–50 m zone(i.e. from 1.8 to 3.6 s), and their focal point is first dedicated tomore long-term anticipation. On the Fig. 9 (dotted circle), it corre-sponds to the intersection centre, and is more specifically centredon the opposite traffic in prevision to implement in the future theleft-turn activity.

In a more qualitative viewpoint, data collected through theICARE-3D tool (including a 3D model of the road infrastructure,underlying the last image of the video sequence) provides alsointeresting results concerning drivers’ mental representations er-rors, in their spatial and temporal dimensions. In this part of theexperiment, participants could use a Visual Feed-Back tool in orderto draw their own mental model (they can suppress or add newcomponent on the final road scene image). Fig. 10 presents anexample of erroneous answer (ICARE sequence no 35) given by

Fig. 10. Example of erroneous mental mode

the participant no 14 (novice male driver), in Dual Task conditions.This erroneous SA is probably due to the attention sharing requiredby the DT, but also to the driving scene complexity during the 4last seconds of the sequence (interaction with three vehicles, anddifficulty to understand the rules of priority; Fig. 10A). Conse-quently, the ICARE modification (added pedestrian; cf. Fig. 10B)was well detected, but the 3D rebuilding of the driving scene doneby this participant was not updated concerning the bus position(Fig. 10C). It seems that, under the lack of attentional resources,his mental model of the driving situation has been partially ‘‘fro-zen” at T less 3 s for this particular vehicle (Fig. 10D shows thebus position at T – 3 s). This example of error illustrates the nega-tive impact of attention sharing in terms of drivers 3D mentalmodel updating, and the interest of a Visual Feed-Back tool tostudy this question.

3.3. From ‘‘Situational Awareness” to ‘‘Risk Awareness”

In this last series of results, we focus on Risk Awareness (RA), i.e.the way in which a driver becomes aware or not of a situation,hitherto normal, but suddenly becomes critical. This topic has beenstudied via several complementary experiments at LESCOT, eachone continuing from the previous. The first was performed onthe open road with an instrumented vehicle. The task of the partic-ipants was to realise a 160 km route through urban areas and oncountry roads, without any other instruction than following thedestination provided by the investigator. Ten experienced driversparticipated in this study. In all, more than 100 situations withan obstacle occurrence were collected. These data were used to de-velop a real-time analysis model of the driving activity in a criticalcondition (Bellet, 2006). The first phase of this model concerns thedriver’s awareness of the situation criticality level. Fig. 11 showsthe type of data collected and the analyses that can be performedin terms of both Situational Awareness (SA) and Risk Awareness(RA).

l (participant no 14, ICARE seq. no. 35).

Fig. 11. Emergency reaction in a critical situation.


In this example, the driver approaches a queue of vehicles wait-ing at roadwork traffic lights. Initially, the driver was perfectlyaware of obstacle occurrence: she released the accelerator, thenshe pressed the brake pedal (circle 1). The speed of the vehicle de-creased, then stabilised at 35 kph (circle 2). At this moment, thedriver was convinced she controlled the situation: from her view-point (which was explained during the self-confrontation interview),since the lights had changed to green the vehicles stopped in frontof her would very soon start up, so it was not necessary to stop un-der these conditions. She therefore released the brake pedal (circle3), considering that her current speed gave her vehicle sufficientimpetus to fall in line at the same pace as the other drivers(hypothesis indicated by the dotted line of circle 4). Unfortunately,her expectations were not confirmed, and the vehicles did notstart. She suddenly became aware of her error. She carried out anemergency reaction on the brake pedal (circle 5), in order to avoida collision. This example shows how it is possible, by using record-ings of driving behaviour, to infer drivers’ SA and RA.

However, in the framework of this type of naturalistic data col-lection performed under real driving conditions, each situation is aunique case. Although it is possible to make generalisations, it isnot possible to compare several drivers’ performances throughabsolutely identical situations, in order to compare their capacitiesto assess and manage the risks of collision. In view to performingsuch comparisons, we selected 21 situations with an obstacleoccurrence recorded during this previous study. Then we designedthe laboratory protocol CRITIC (cf. 2.3). According to this method,the participants must view these video sequences and stop themwhen they judge the situation is becoming critical. Then, at theend of the sequence (previously interrupted or not), each situationis assessed by the participants via a double scoring: a criticality le-vel estimation (i.e. a score from 0 to 100%, depending on the posi-tion of a cursor on a non-graduated Likert scale) and a valuejudgement on the situation, by using Osgood’s semantic differential(including 16 antonyms). In a recent study (Banet and Bellet,2008), this protocol was used to compare the Risk Awareness oftwo populations of road users: 10 car drivers, and 11 motorcyclists.Among the results obtained, the most interesting in the framework

of this article concern the identification of different categories ofdriving situations, in relation to the road user profile. Thus four cat-egories could be distinguished from our panel of 21 situations: sit-uations judged critical by all the participants (seven cases),situations judged non-critical by all the participants (four cases),situations judged critical only by car drivers (eight cases, mainlywhen approaching of large obstacles, or pedestrians), and situa-tions judged proportionally more critical by motorcyclists thanby car drivers (two cases, in wet weather). Analysis of the resultsobtained by the semantic differential gave a great deal of informa-tion. For the motorcyclists, the situations appeared more fre-quently as less dangerous, and better controlled. They generallyfelt more involved and responsible for changing events. By contrast,the car drivers tended to consider more frequently that they suf-fered the situation, and felt less often in control of events. In all, thisprotocol permits understanding and measuring inter-populationdifferences comparatively regarding SA and Risk Awareness. Notall of them represent the risks and the situation in the same way,but according to their road user profile. As vulnerable users, themotorcyclists felt more involved and responsible but, on the otherhand, they estimated the situations on average as less critical. Inorder to refine these analyses, further comparative studies are inprogress using a large population of motorcyclists and combiningdifferent sources of variations, such as driving experience, ormotorcyclist profile (e.g. racer, rambler, urban user, police motor-cyclist) and attitude towards risk and/or risk-taking.

In complementary with ICARE method, more focused on thedrivers Situation Awareness, CRITIC protocol provides specific re-sults concerning the reflexive awareness (cf. 1.6) associated withthis SA, taking the form of a value judgement regarding the situa-tional risk of collision. Risk awareness is mainly based on theassessment of path conflict risks with other road users, and onthe hazard anticipation at a more or less long term, according toboth (i) others’ behaviours and (ii) oneself intentions or actions.In order to jointly investigate SA and RA, it is nevertheless neces-sary to develop a computational model able to simulate dynami-cally the drivers’ mental representations implementation whiledriving, and the drivers ‘‘mental simulation” abilities.


4. Discussion: how can representations be represented, thensimulated ?

Each of the experiments described above highlights certain spe-cific characteristics of the drivers’ mental representations. In thislast part, we propose an integrated approach to these resultsthrough a computer model developed at LESCOT: COSMODRIVE(COgnitive Simulation MOdel of the DRIVEr). The general aim ofthis model is to produce a computer simulation of the driver’smental activity (from information taking to behaviour implement-ing). Concerning its cognitive architecture, COSMODRIVE is directlybased on Fig. 1 (cf. 1) structure, and distinguishes a Working Mem-ory (containing occurrent representations), and a Long Term Mem-ory (that contains permanent knowledge: the ‘‘driving schemas”).Computer-wise, the model is based on a distributed intelligenceapproach through a multi-agent architecture: WM is implementedvia two blackboards that allow several cognitive agents (e.g. recog-nition or categorisation of the situation, representation generator,decision-making, anticipation, cognitive resource management)to cooperate around a shared representation of the driving situa-tion (Bellet et al., 2007). This occurrent representation, formulatedcollectively by the cognitive agents, is at the core of the COSMO-DRIVE’s regulation loop for car driving. This regulation loop is moreparticularly based on the (i) selection, (ii) instantiation, (iii) deploy-ment, and (iv) implementation of the driving schemas.

4.1. Driving schemas: towards a computational formalism toimplement operative knowledge and drivers mental representations

The concept of ‘‘driving schemas” is derived from the worksdone in the 1970s and 1980s devoted to computer modelling ofnatural human knowledge. Among the reference theories, the for-malism provided by Minsky (1975) frames is particularly appropri-ate for modelling driving knowledge. Developed in the frameworkof intelligent robotics, the frames theory is based on Piaget’s workon Operative Intelligence. It is precisely this double appreciation offrame and schemes that can be found in the driving schemas of COS-MODRIVE. As with Minsky’s frames, the driving schemas formstructured blocks of knowledge and more or less generic operativemodels, formulated on the basis of practical experience. They

Fig. 12. 3D modelling of the ‘‘drivi

correspond to prototype situations, sequences of actions andevents. Like Piaget’s schemes, driving schemas are capable ofassimilation and accommodation, providing them with a certain de-gree of plasticity in relation to reality. Confronted by a specific sit-uation, the cognitive system activates one of these assimilatingstructures available in LTM. This leads to a matching process, per-mitting the instantiation of the schema in the specific frameworkof the driving situation occurring at a given moment. From thecomputational standpoint, instantiation is tantamount to assigningnew values to schema attributes, through updating the default val-ues from perceived data. Certain attributes of the schema concernthe descriptive characteristics of the road scene (e.g. the visuo-spa-tial model of the infrastructure), whereas others refer more di-rectly to the operative activity (e.g. functional breakdown of theroad environment, in relation with the goal to be achieved in thisinfrastructure; cf. 3.1). It is not necessary for instantiation to becomplete for the schema to be used. Quite the contrary: the advan-tage of having default values in the schema ensures that action ispossible without systematically relying on the acquisition of allthe information. These default values also allow the driver to inferinformation not directly accessible in the environment. The attri-butes of the driving schemas can be more or less constrained. Ifthey are integrated in the form of numeric intervals or lists of pos-sible values, then the schema will be more generic, with greatercapacities for assimilation, proportional to the range of possiblevalues. By contrast, a schema whose attributes are highly con-strained corresponds to more specialised knowledge. Furthermore,filling in and validating certain attributes can be imperative, there-by forming the schema’s validity limits. If the schema is incompat-ible with reality, it cannot be used to categorise the drivingsituation, so another schema must be sought in LTM. Regardingthis instantiation process, the mental representation of the situationis defined in COSMODRIVE as a specific occurrence of a driving sche-ma, activated in WM, and instantiated with reality. Once formulated,this representation allows the driver to mentally project the func-tional breakdown of the road space contained in the schema ontothe real infrastructure (cf. Fig. 12). The driving task is thereforeachieved by performing this schema by progressively travellingthrough the driving zones composing it, until the tactical goal isreached (e.g. left turn).

ng schemas” in COSMODRIVE.


4.2. The ‘‘envelope zones”: a central concept for risk awarenessmodelling

Nonetheless, this first set of cognitive processes is not sufficientto provide a safe driving behaviour. To progress safely in the roadenvironment, the drivers must also take dynamic events into ac-count, in order to regulate their interactions with the other roadusers. This regulation process of inter-driving path managementconflicts is performed in COSMODRIVE by way of envelope zones.The concept of envelope zones recalls two classical traditions inpsychology: the notion of body image (Schilder, 1950), and the the-ory of proxemics proposed by Hall (1966), relating to the distancemaintained in social interaction with others. Regarding car driving,this idea can also be found in the notion of safety margins (Gibsonand Crooks, 1938), reused by different authors. Thus, for example,Kontaratos (1974) defined two safety zones (respectively, collision

Fig. 13. The ‘‘Envelope Zones”.

Fig. 14. UML diagram o

and threat) in which no other user should enter. If this occurs,the driver systematically performs an emergency reaction. As forOhta (1993), he showed how these safety zones come into playwhen vehicles follow each other. The author defines three follow-ing distances: a danger zone (when the target time headway [i.e.Time-to-Target] is less than 0.6 s), a critical zone (from 0.6 to1.1 s) and a comfort zone (from 1.1 to 1.7 s). Regarding ICARE re-sults, these three zones correspond to (i) the nearby zone and (ii)the 15–25 m zone, previously presented in Section 3.2. The enve-lope zones of COSMODRIVE, defined as an extension of the bodyimage around the car, considered in terms of its spatial–temporaldynamics, therefore incorporates these different approaches.

Three envelope zones are distinguished (Fig. 13): (i) a proximaldanger zone, in which any intrusion immediately triggers an emer-gency reaction, (ii) an intermediate threat zone, in which any non-expected intrusion is considered as potentially critical or aggres-sive, and (iii) and a distant safety zone, corresponding to the regu-lation distance that the driver judges as safe. These zones are called‘‘relative”, in contrast to driving or perceptive exploration zones ofschemas (which are qualified as absolute zones), in so far as theirdimensions are not determined in relation to fixed componentsof the road infrastructure, but in relation to the dynamics of thedriven vehicle. They are effectively based on Time-to-Target values(from 0.5 to 1.8 s) and therefore directly depend on the vehicle’sspeed and driving path. The size of these zones can also varyaccording to driver profile and situational context. This‘‘schematic” skin is constantly active. As with the body image, itbelongs to a highly integrated cognitive level (i.e. implicit regula-tion loop), and at the same time favours the emergence of criticalevents in the driver’s explicit awareness (i.e. risk awareness process,cf. 3.3). By consequence, the envelope zones play a decisive role inthe regulation of interactions with other road users under normaldriving conditions (e.g. maintaining inter-vehicle distances), and

f driving schemas.


in the risk diagnosis or conflicts management of in critical situa-tions (commitment of emergency reactions).

4.3. Computational modelling of the driving schemas in COSMODRIVE

On the formal level, the driving schemas of COSMODRIVE aremodelled by using the Object Oriented Method UML (Bellet andTattegrain-Veste, 1999). Fig. 14 presents this formalism: A drivingschema is defined by a Tactical Goal to be reached in a given Road-way Infrastructure.

It is composed of a Driving Path, itself defined as a sequence ofdriving Zones, and a number of (Simple or Complex) Actions. The

Fig. 15. Computational simulation of drivers m

implementation of these actions depends of Conditions to be con-formed to and checked regarding the occurrence of Events in cer-tain Zones of the infrastructure (Absolute Zones, like Driving andPerceptive Exploration Zones, or Relative zones, like envelopezones). An event is defined by the occurrence of an Object with spe-cific Characteristics (describing its appearance, behaviour, or sta-tus). The term ‘‘object” is used here in its widest meaning. It canbe a vehicle, a pedestrian, or a road sign. The schema implementa-tion is done by Operational Units. The procedure of implementing adriving schema entails deploying it. This deployment (simulated oncomputer by Mayenobe, 2004) consists in moving the vehicle alonga driving path, by successively travelling through the different

ental representations with COSMODRIVE.


driving zones of the schema, from the initial state (entry zone)until reaching the tactical goal (exit zone). This deployment proce-dure occurs at two levels: (i) on the representational level (explicitor implicit mental simulation), when the drivers anticipate andproject themselves mentally in the future, (ii) and through theactivity itself, during the effective implementation of the schemawhile driving the car in the road environment. This twofolddeployment process is not performed by a specific agent in COS-MODRIVE. It is an emergent dynamic process, resulting from thecombined computations of several cognitive agents (Mayenobeet al., 2006). As a result, the deployment process generates aparticular instance of schema execution, composed of a temporalsequence of occurrent representations, causally interlinked, corre-sponding to the driving situation as it was progressively under-stood, anticipated, experienced, and lastly acted by the driver,along the driving path progression.

4.4. Illustration of experimental results simulation, via COSMODRIVEmodel

By considering (i) ICARE results concerning SA (cf. 3.2; Figs. 8and 9), (ii) the driving activity analysis while turning on the leftat an urban crossroads (including naturalistic observations anddrivers’ interviews, cf. 3.1), and (iii) the driving schema formalismdesigned for operative knowledge modelling, it is then possible touse the COSMODRIVE model to dynamically simulate the driversmental representations implementation during driving. The fol-lowing figures provide some examples of such cognitive simula-tions. Typical ICARE differences, according to the drivingexperience (Novices vs. Experienced Driver) and the quantity ofattentional resources available, are presented through these exam-ples. In this situation, the drivers are going up to an urban intersec-tion (Fig. 15a) with the tactical goal: ‘‘turn on the left” (Fig. 15b). Byconsidering the driving experience effects in terms of focal pointdifferences (centred on the nearby zone for Nov. [Fig. 15d]/on theintersection centre for Exp. [Fig. 15c]), Experienced drivers’ SAare generally more accurate than novices’ SA concerning the trafficflow in the intersection.

Moreover, attention sharing conditions (DT) tend to increaseagain these differences. For novices, the lack of cognitive resourcesreduces the focalisation of their attention on the nearby zone. Con-sequently (Fig. 15f) their Situational Awareness is essentially lim-ited to their immediate environment (i.e. Z1 zone of the currentdriving schema). On the contrary, experienced drivers focused atfirst their residual attention on the 25–50 m zone (i.e. Z3 zone ofthe driving schema). Consequently (Fig. 15e), if some secondarytraffic events are missing (e.g. the car turning of the right), theyare generally aware since this time of the most significant event,i.e. the opposite car, according to their future tactical goal imple-mentation (‘‘turning on the left”). These differences are alsodepending of the drivers’ anticipation abilities (i.e. mental simula-tion of the driving situation future status based on the drivingschema deployment), requiring driving experience and cognitiveresources. For novice drivers, more cognitive resources are re-quired for the car driving monitoring and the management of theirinteractions with the nearest events. Consequently, their anticipa-tion abilities are limited, and they generally neglect more long-term status, or event. By contrast, skill-based behaviours imple-mented by experienced drivers allow them to allocate their cogni-tive resource for anticipation. They can mentally simulate theirfuture ‘‘turning-left” manoeuvres in order to make decision andadequately plan their behaviours. As a result, the majority of theexperienced drivers are aware, from this approaching phase ofthe intersection (Z1), of an opposite car occurrence, and of the po-tential path conflicts with it in the future (Z3).

5. Conclusion and perspectives: towards an ‘‘operativerepresentation of operative representations

The aim of this article was to focus on the drivers’ mental rep-resentations, as they are carried out in the specific context of car-driving activity. In the introduction, we discussed several theoret-ical questions relating to the nature of these representations ‘‘foraction”, and the role they play in situational awareness as in thedrivers’ regulation of their operative activity. The second part dealtwith the ‘‘experimental continuum” of the methods set up at LES-COT for the scientific investigation of functional representations,alternating naturalistic observations of the driving activity on realroads with more controlled, systematic and reproducible labora-tory experiments. We then presented (Section 3) several significantresults obtained by using these methods, before proposing (in Sec-tion 4) an integrated model of these mental representations in theform of ‘‘instantiated driving schemas” in Working Memory.

The advantage of the driving schema formalism is to combinedeclarative and procedural knowledge in the unified computa-tional structure. This characteristic makes the schemas very inter-esting from the standpoint of operative knowledge modelling andthe driver’s mental representations simulation. In accordance withActivity theories, these cognitive structures guarantee a continuumbetween the different levels of (i) awareness (implicit vs. explicit)and (ii) activity control (tactical vs. operational), thereby taking fullaccount of the embedding of operative know-how (i.e. the level ofimplementation) in the explicit and decisional regulation loop ofthe activity.

Lastly, in the same way as the driving activity fuels itself di-rectly with operative representations, the driver’s operative repre-sentations are also fuelled ‘‘by” the activity, and ‘‘for” the activity,according to a double deployment process: cognitive and represen-tational on the one hand, and sensorial-motor and executive on theother. From a metaphorical standpoint, a driving schema can becompared to a strand of DNA. It potentially contains all the behav-ioural alternatives that allow the vehicle to progress within a moreor less generic class of situations. Nonetheless, only a tiny part ofthese ‘‘genes of alternatives” express themselves in the situation– with respect to the constraints and specific characteristics ofreality – during the cognitive (via instantiation and deploymentof mental representations), and then executive implementationof this schema (via the effective activity carried out to drive thecar). And it is only during this process of implementing operativerepresentations that certain of their intrinsic properties willemerge. Consequently, scientific investigation of drivers’ mentalrepresentations cannot forego the use of computer models of theoperative simulation of these operative representations, withouttaking the risk of being largely incomplete.

The COSMODRIVE model is specifically designed in order todynamically simulate the drivers’ mental representations in theirspatial, temporal and operative dimensions (corresponding to theirimplicit and explicit awareness of the driving situation). In its cur-rent status, COSMODRIVE development is limited, more particu-larly concerning the behavioural simulations. By contrast, othermodels of the driver presented in the literature seem more ad-vanced concerning the driving performance simulation and/orthe perception–action loop of control implementation (e.g. Songet al., 2000; Salvucci et al., 2001; Krajzewicz et al., 2004; Liu andOzgüner, 2007), and some of them also integrate mental workloadeffects on the driving task performance (e.g. Salvucci, 2001; Wuand Liu, 2007). Nevertheless, drivers’ mental representations sim-ulation is generally missing in these models, or only basically sim-ulated (e.g. through a set of explicit rules). Cognitive simulationmodels based on a 3D operative modelling of the drivers’ mentalrepresentations are then very complementary with these perfor-


mance-based approaches, and are also required in order to providemore comprehensive models of the car driver in the future.

Thus, concerning the perspectives of the COSMODRIVE researchprogramme, the next step is now to extend the development of theoperational module of the model, in interaction with a virtualmodel of the road environment. This work is in progress by usinga virtual Driver-Vehicle-Environment platform called SIVIC (Gru-yer et al., 2006), with the aim to develop a joint Cognitive-Behav-ioural model (i.e. including both mental activities and drivingbehaviour simulation) on SIVIC. Then, it will be possible to directlyintegrate the data collected via instrumented car on this DVE vir-tual platform, and consequently to assess COSMODRIVE validityin comparing the behaviour of the model with real drivers’ activityobserved through empirical studies.

Acknowledgement

In memory of Joseph Bernard Bellet (1940–2008), who diedwhile this article was being written.

References

Anderson, J.R., 1993. Rules of the Mind. Lawrence Erlbaum Associates, Hillsdale,New Jersey.

Atkinson, R.C., Shiffrin, R.M., 1968. Human memory: a proposed system and itscontrol process. In: Spence, K.W., Spence, J.T. (Eds.), The Psychology of Learningand Motivation, vol. 2. Academic Press, New York, pp. 89–195.

Baddeley, A.D., 1986. Working Memory. Clarendon Press, Oxford.Bailly, B., 2004. Conscience de la Situation des conducteurs: aspects fondamentaux,

méthodes et applications pour la formation des conducteurs. Thèse de Doctorat,Université de Lyon 2.

Bailly, B., Bellet, T., Goupil, C., 2003. Driver’s mental representations: experimentalstudy and training perspectives. In: Dorn, L. (Ed.), Driver Behaviour andTraining. Ashgate, pp. 359–369.

Banet, A., Bellet, T., 2008. Risk awareness and criticality assessment of drivingsituations: a comparative study between motorcyclists and car drivers. IETIntelligent Transport Systems 2 (4), 241–248.

Bedny, G., Meister, D., 1999. Theory of activity and situation awareness.International Journal of Cognitive Ergonomics 3 (1), 63–72.

Bellet, T., 1998. Modélisation et simulation cognitive de l’opérateur humain: uneapplication à la conduite automobile. Thèse de Doctorat, Université RenéDescartes Paris V.

Bellet, T., 2006. Driving activity analysis and modelling for adaptive collisionavoidance systems design. In: Proceedings of the 16th World Congress onErgonomics (IEA), Maastricht, The Netherlands, 10–14 July 2006 (On CD-Rom).6 p.

Bellet, T., Tattegrain-Veste, H., 1999. A framework for representing drivingknowledge. International Journal of Cognitive Ergonomics 3 (1), 37–49.

Bellet, T., Bailly, B., Mayenobe, P., Georgeon, O., 2007. Cognitive modelling andcomputational simulation of drivers mental activities. In: Cacciabue, P. (Ed.),Modelling Driver Behaviour in Automotive Environment: Critical Issues inDriver Interactions with Intelligent Transport Systems. Springer Verlag, pp.315–343.

Bisseret, A., 1970. Mémoire opérationnelle et structure du travail. Bulletin dePsychologie 24 (5–6), 280–294.

Endsley, M.R., 1995. Toward a theory of situation awareness in dynamic systems.Human Factors 37 (1), 32–64.

Ericsson, K.A., Kintsch, W., 1995. Long-term working memory. Psychological Review102, 211–245.

Fodor, J.A., 1975. The Language of Thought. Thomas Y. Crowell, New York.Gibson, J.J., Crooks, L.E., 1938. A theoretical field-analysis of automobile driving.

American Journal of Psychology 51, 453–471.Graf, P., Schacter, D.L., 1985. Implicit and explicit memory for new associations in

normal and amnesic subjects. Journal of Experimental Psychology: Learning,Memory, and Cognition 11, 501–518.

Gruyer, D., Royère, C., du Lac, N., Michel, G., Blosseville, J.M., 2006. SIVIC and RT-MAPS interconnected platforms for the conception and the evaluation of drivingassistance systems. In: Proceedings of the ITS World Congress London UK,October 2006.

Hall, E.T., 1966. The Hidden Dimension. Doubleday, New York.Hoc, J.M., Amalberti, R., 1995. Diagnosis: some theoretical questions raised by

applied research. Cahiers de Psychologie Cognitive 14 (1), 73–101.Johnson-Laird, P.N., 1983. Mental Models: Towards a Cognitive Science of Language,

Inference, and Consciousness. University Press, Cambridge.Koch, C., 2004. The Quest for Consciousness: A Neurobiological Approach. Roberts &

Co. Publishers, Engelwood.Kontaratos, N.A., 1974. A system analysis of the problem of road casualties in the

United States. Accident Analysis and Prevention 6, 223–241.Krajzewicz, D., Hühne, R.D., Wagner, P., 2004. A car driver cognition model. In:

Proceedings of 2004 ITS Safety&Security World Conference, 24–25 March,Miami Beach, Florida, USA.

Leontiev, K., Lerner, A., Ochanine, D., 1961. Quelques objectifs de l’étude dessystèmes ‘‘Homme-Automate”. Question de Psychologie 1, 1–13.

Leplat, J., 1985. Les représentations fonctionnelles dans le travail. PsychologieFrançaise 30 (3/4), 269–275.

Leplat, J., 2004. L’analyse psychologique du travail. Revue Européenne dePsychologie Appliquée 54, 101–108.

Liu, Y., Ozgüner, Ü., 2007. Human driver model and driver decision making forintersection driving. In: Proceedings of the 2007 IEEE Intelligent VehiclesSymposium, Istanbul, Turkey, 13–15 June (on CD-Rom). 6 p.

Mayenobe, P., 2004. Perception de l’environnement pour une gestion contextualiséede la coopération Homme-Machine. Thèse de Doctorat, Université Blaise Pascalde Clermont-Ferrand.

Mayenobe, P., Bellet, T., Trassoudaine, L., 2006. Simulation cognitive de l’activitéperceptive et opératoire du conducteur. In: Proceedings of the 2006 ConférenceInternationale Francophone d’Automatique (CIFA 2006), Bordeaux, 30 May–1stof June (on CD-Rom). 6 p.

Michon, J.A., 1985. A critical view of driver behavior models: what do we know,what should we do? In: Schwing, R.C., Evans, L. (Eds.), Human Behavior andTraffic Safety. Plenum Press, New York, pp. 485–520.

Minsky, M., 1975. A framework for representing knowledge. In: Winston, P.H. (Ed.),The Psychology of Computer Vision. Mc Graw-Hill, New York, pp. 211–277.

Norman, D.A., 1983. Some observations on mental models. In: Gentner, D., Stevens,A.L. (Eds.), Mental Models. Lawrence Erlbaum, pp. 7–14.

Ochanine, V.A., 1977. Concept of operative image in engineering and generalpsychology. In: Lomov, B.F., Rubakhin, V.F., Venda, V.F. (Eds.), EngineeringPsychology. Science Publisher, Moscow.

Piaget, J., 1974. La prise de conscience. Presses Universitaires de France, Paris.Proust, J., 2003. Les Animaux Pensent-ils. Bayard, Paris.Rasmussen, J., 1986. Information processing and human–machine interaction: an

approach to cognitive engineering. North Holland, Amsterdam.Richard, J.F., 1990. Les activités mentales: comprendre, raisonner, trouver des

solutions. Armand Colin, Paris.Salvucci, D.D., 2001. Predicting the effects of in-car interface use on driver

performance: an integrated model approach. International Journal of Human-Computer Studies 55, 85–107.

Salvucci, D.D., Boer, E.R., Liu, A., 2001. Toward an integrated model of driverbehavior in a cognitive architecture. Transportation Research Record 1779, 9–16.

Scheinder, W., Shiffrin, R.M., 1977. Controlled and automatic human informationprocessing I: detection, search and attention. Psychological Review 84, 1–88.

Schilder, P., 1950. The Image and Appearance of the Human Body. InternationalUniversities Press, New York.

Smirnov, A., 1966. La mémoire et l’activité, Recherches Psychologiques en URSS,Editions du Progrès, Moscou.

Smolensky, P., 1989. Connectionist modeling: neural computation/mentalconnections. In: Nadel, L., Cooper, L.A., Culicover, P., Harnish, R.M. (Eds.),Neural Connections, Mental Computation. The MIT Press, Cambridge MA, pp.49–67.

Song, B., Delorme, D., VanderWerf, J., 2000. Cognitive and hybrid model of humandriver. In: Proceedings of the 2000 IEEE Intelligent Transportation SystemsConference, Dearborn (USA), October 2000. pp. 1–6.

Stanton, N.A., Chambers, P.R.G., Piggott, J., 2001. Situational awareness and safety.Safety Science 39, 189–204.

Tulving, E., 1983. Elements of Episodic Memory. Oxford University Press, London.Vergnaud, G., 1985. Concepts et schèmes dans la théorie opératoire de la

representation. Psychologie Française 30 (3), 245–252.Vermersch, P., 1994. L’entretien d’explicitation (en formation initiale et en

formation continue). ESF Editeur, Paris.Wu, C., Liu, Y., 2007. Queuing network modeling of driver workload and

performance. IEEE Transactions on Intelligent Transportation Systems 8 (3),528–537.

Zintchenko, P., 1966. Quelques problèmes de psychologie de la mémoire,Recherches Psychologiques en URSS, Editions du Progrès, Moscou.

a theoretical and methodological framework for studying and modelling drivers’ mental...

Documents