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Truck driver's behaviour and rational driving assistance

*A. Maincent(1), R. Martin(1), S. Fornengo(2)(1) LEACM/Université Lyon2/Renault Trucks (2) Renault Trucks/Auxirbat Recherche, France

Annick.Maincent@ish-lyon.cnrs.frAbstract

This work is performed as collaboration between the Laboratory for Studies andAnalysis of Cognition and Models, Renault Trucks and Auxirbat Recherche.Today, the fuel consumption of a truck, apart from the technical characteristics ofeach model, primarily depends on the driving style and interactions between driver,vehicle and road environment. The goal of the project is to provide the truckconstructor with a truck driver’s behaviour model in terms of a rational driving, inorder to develop an adaptive eco-driving assistance. The vehicle electronic controlunit will identify the driving styles and take driver’s behavioural variability intoaccount.Driving activity is defined as a motor and cognitive skill acquired with training andpractice. It induces an essentially mental workload that will be variable according toexternal factors (road environment, temporal demands, etc.) and internal driver factors(expertise, personality, cognitive style, etc.).In this research, we propose a methodological approach to study truck drivers’behaviours by psychological and ergonomic methods. An experimental procedure in adynamic operational environment will enable:

- To validate a workload index for driving tasks,- To categorize truck drivers according to economical driving styles and

cognitive styles,- To translate driving behaviours into objective and measurable criteria,- To identify bad behaviours that an assistance system can correct in order to

decrease fuel consumption.By recording the driver’s actions on different vehicle switches and controls(accelerator, brake, gear, etc.) we can draw the driver’s behaviours, with a specificsoftware. In addition, the drivers must fill in several questionnaires related topersonality, representations, and cognitive style and workload assessment.The first results have allowed integrating the relevant driving criteria to a simulationtool. They also have pointed out an important intra-individual variability concerningthe rational driving behaviours: most of the drivers show a significant consumptiondifference, on the same course, before and after training in an economical drivingstrategy.

IntroductionThe actual economical context appears to favour trading economy so it makes theimportance of optimising costs stand out whether they are public or private. And yet itturns out that private running costs of a truck mainly depend on:

- Drivers’ wages and social security contributions (32%),- Vehicles fuel consumption (22%),- Vehicles maintenance (10%).

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Whereas public costs mainly come from up keeping and improving road infrastructure(proportional to traffic and numbers of accidents) as well as fighting against chemicaland sonorous pollution.Cutting down the consumption of heavy vehicles leading to a decrease of exhaust gasor greenhouse effect gas as well as reducing particles emissions has become, in thisway, a major economical purpose for manufacturers. While most existing works focuson optimisation of the machine, the project “Economical driving assistance” suggestsconsidering the problem through a systemic point of view, integrating the behaviouralprofile of the driver into the driving loop.The present vehicles are sensitive to the driving style: road transport firms havenoticed disparities of more than 10% in the fuel consumption between differentdrivers on the same assignment. Comparatively, the work about the output of the driveline chain (aerodynamics, new oils, new tyres) only provide minor profits because thehigh output reached for the kinematics chain of present vehicles does not allow a highmargin of progress.As far as rational driving is concerned, there are critical ranges of engine, whichcontribute towards an optimal efficiency. However, if the driver does not use theseranges, of his own free will (motivation) or unwillingly (tiredness, inattention, lack ofknowledge, etc.), the result is an excessive fuel consumption as well as a fast wear ofthe drive line components (brakes, tyres, gearbox, etc.). Moreover, anticipation is abasic aspect of driving. It may be wise, for a while to get out of the optimal efficiencyranges to anticipate an event in order to globally save fuel (ex. to gather speed beforean important difference in height or switch off fuel injection 100 meters before thebreak in slope rather than using brakes.)These acknowledgements suggest that is the strategy implemented by the driver,which mainly creates a rational way of driving. Through his behaviour and his way ofdriving the driver plays an important economical part in the productivity of a heavyvehicle.Thus, prior the optimisation of the kinematics chain, the target of the project is todevelop an adaptive rational driving assistance, to help the driver to pilot his vehicleaccording to the best strategy. The “in-board supervisor” in charge of this task willplay the part of a co-pilot, not only relying on data coming from environment andvehicle, but also on the knowledge of drivers’ behaviours and driving styles. Thesystem will only have to rectify wasteful practices without hindering drivers whoseways of driving are correct. Besides this challenge, the precision of the informationwill be provided to the driver requires a careful work about the Human MachineInterface (HMI).To answer these purposes concerning different technical and scientific fields, theglobal project has been organised according to two complementary main lines.

1. A technical and theoretical work was taken in charge by engineers of theAdvanced Engineering Department of Renault Trucks to study and conceivethe technical system,

2. At the same time, man being in the centre of this scheme, a psycho-sociological and behavioural study about truck drivers is being made by theLaboratory for Studies and Analysis of Cognition and Models (LEACM) ofthe Lyon 2 University.

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This paper introduces the contextual and methodological background of the secondresearch main line, which is focused on the driver. The partnership takes place in thecontext of a three years’ research agreement concluded between the LEACM andRenault Trucks. All the researches are structured around a doctoral thesis in cognitivepsychology “Research about interactions between technical progress and humanbehaviours with the idea of lasting development: about the economical drivingassistance of a heavy vehicle”. This thesis is mostly focused on economical,psychological and cognitive aspects of truck driver’s behaviours in order to a rationaldriving

1. Background1.1. Road transport of goods

People and goods transport has a considerable place in the economical activity of ourmodern society. Road transport of goods throughout Europe is nowadays a flexibleand adaptable basic multifaceted activity essential to the development of a country1.However, even though its cost price is still reasonably economical2, it could grow inthe near future3.In France, the haulage of goods is the first road transport sector (78%) and 42,000firms, which hold more than 230,000 drivers, work in this sector. The activity of thesefirms is highly diversified (international, national and regional, urban and suburbanareas, messenger service, etc), and the sort of transported products itself is even morediversified (refrigerator, tank, food, hazardous materials, etc.).In fact, more than 90% of consumables of daily life (ranging from food to electricalappliances and pharmaceutics goods to fuels without forgetting cultural products) aretransported on roads. Alone in a position to afford the door-to-door transport, thetruck is complementary to all the other means of transport. The road transport ofgoods proves to be an irreplaceable agent of life and development (Hillmeyer, 2004).

1.2. Road transport of goods and long-term developmentHowever, road transports consume fossil energies and so represent the first source ofatmospheric pollution and greenhouse effect gas, which is the short, or medium termthreat for our health and way of life. Many analysts consider that in the next 20 yearsthe truck traffic will go on growing on the one hand and on the other hand, the heatengine using fuel oils will mostly remain the favourite system of motorization for carsand heavy vehicles (Pinchon, 2003). A strong action seems necessary to protect ourplanet while preserving our freedom needs, thus our mobility.If there are no short-term miracle solutions, however it is possible to cut downpollutions coming from transports by actions about- Drivers’ behaviours- Technological innovations- Development of “clean methods”To some extend the long-term development must allow a fair access to energy andtransports for everybody and for a long time, keeping in mind the worry of preserving

1 The transport at a low rate is a necessary condition to globalisation.2 Between air transport on the hone hand and on the other hand, rail, sea and fluvial transports, which

are cheaper but not as flexible: see transports in Holland (fluvial transport at 40%) in the Danubedistrict.

3 Because fuel oils of fossilized origin should become rarer and rarer in the next 40 years.

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natural harmonies. Then the conciliation of mobility, environment protection andnatural resources is a major stake for today and even more for tomorrow.Therefore, energy and transports are in the centre of the paradoxes of long-termdevelopment. It is true that they are both essential to the development of a country andto its socio-economical growth, but their actual evolutions are risking the running outof non renewable natural resources and deterioration of environment. Having a long-term answer to the great challenges of mobility requires the consideration of manyconcerns about environment and energy.The French strategy of long-term development, in its transport section, has listed thetargets and actions, which are set up to reduce the environmental transports impacts.Considering the energy consumption we can notice to get a better consideration of theimportance of the energetic stakes by everybody: (1) the technological improvementsproviding the development of new energies (2) the best use of our means of transportand (3) the behaviours we can change.As far as transports are concerned as well as long-term development, technologicalinnovations and actions about human behaviours come at the top of the list of theresearch problematic. Thus, a calm driving which respect of the speed limits and thesecurity distances with fewer accelerations and strong braking will directly contributetowards saving lives but as well to significantly cut down the fuel consumption anddamaging emissions, and using with care the vehicle equipments. Therefore, thedriving behaviours of truck drivers represent an important stake for the problem oflong-term development in the context of road transports.

1.3. Driving and new technologies of assistanceIn the near future by turning towards the automation way, road vehicle will get nearother technical systems such as plane or nuclear industry. It will benefit by theacquired experiences in other respects to turn towards a globally more reliable andsafer traffic system. Moreover, in the road field, the driver has always beenconsidered as a component of the system causing most of the poor running. Thestudies bring to the fore that at least one human factor is involved in 70 to 90% of theaccidents, whereas the proportion for road infrastructure is 15 to 35% and the onebelonging to vehicle is 5 to 13%. That’s why today with work of vehiclemanufacturers, authorities and local administrators, the target is the design of“intelligent vehicles” gaining more and more computing developments, able totransport people and goods in an efficient ecological and safe way (Stich, 2004).Nevertheless, the quick expansion of new technologies entails new problemsparticularly psychological ones, which must be taken into account here and now.Paradoxically the addition of “intelligent” systems in trucks increases the drivers’mental workload. The driver not only must pilot his vehicle ensuring theresponsibility of his goods and the security of other road users but also must manage alot of systems, supply instructions, watch the road environment, communicate withthe outside world, etc. Even more deeply, these systems alter the truck driver’s job.Therefore, it is necessary not to neglect the final user for fear of developing systems,which will not be accepted (or be badly accepted) by drivers thus underused if notrejected or twisted.

1.4. Truck driving activityThe diversity of the road system as a dynamic environment constrains the driver toadapt himself to varied situations, which evolve in an independent way (Amalberti,

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1996). This permanent adjustment is submitted to dynamic and temporal constraints,on the one hand, coming from the moving and the control of the vehicle in the roadenvironment, and on the other hand, from the behaviours of the other road users.Furthermore, the truck driver’s activity is submitted to rules, which are both explicit(road regulations, work code, etc.) and implicit (self rules, vernacular rules, etc.) towhich strong spatio-temporal constraints due to the road transport activity can beadded.For this reason, the truck driving activity can be considered as complex thoughrelying on a certain number of cognitive skills and the automation level of themmainly depends on the driver expertise (Perruchet, 1988). The tasks, which make upthis particular activity, can alternate or can be carried out simultaneously and theyneed available resources of attention. However, the attention demand isn’t continuous,and the variability of the driving activity is accorded to the demands of the system(Sperandio, 1995). Furthermore we must add driver’s psychological and physiologicalfactors like his motivation, his personality, his cognitive style, his tiredness, hispossible stress level, etc. Finally the truck driver’s activity commits his responsibilitytowards the road system as well as towards his firm and its customers.

1.5. Rational drivingAlthough the project is based on the fuel consumption, we prefer to talk about“rational” driving. It is right, even if the fuel costs exceeded 20% of the overall costsof transport firms, that the productivity of a truck is not only measured in term of fuelconsumption but also in terms of use of the drive line components, of the costs ofmaintenance and above all, the variable-key of the system, in terms of delays fordelivery. A system that would optimise the vehicle fuel consumption for aconsideration of a waste of time would not be compatible with the actual exploitationpolicy of road transport firms.Rational driving can be defined as a compromise between the average speed and thefuel consumption4. As it is recommended in training centres, rational drivingoptimises this ratio while treating with care the components of the kinematics chain(gear, brakes, pneumatics, etc.), with the result of an increase in security and economyas well as in the driving comfort.Nevertheless, there is a problem left, the training benefits do not seem to last long.Drivers rather quickly find their previous driving automatisms again which may begood or bad, in spite of getting a correct explicit knowledge of the applicationprinciples concerning the rational driving.

2. Cognitive and ergonomic approachThe actual direction in the conception approach of varied driving assistances isn’t tosubstitute man for machine but to bring a clever help to the driver. As far as cognitiveengineering is concerned, the cognitive psychology approach is turned towards aconception idea focused on Human (a “Human like” technology), with the purpose ofcreating a cognitive transparency between driver and the system as well as betweenthe system and driver (as an invisible hand). The approach is going to favour theidentification, the description and the explanation of the varied components of the

4 For example, an optimal consumption would need an average speed of 80 km per hour versus the

average optimal speed will be of 90 km per hour if we add wages and delays for delivery toconsumption.

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truck’s driver activity, which cause the differences in consumption. Thanks to theused methods, the results of the research will enable to adjust the conception of thenew system to make it suitable to the particularities of the drivers’ behaviour and tothe truck driving activity.The works are based on the idea of a complex overall system mainly coupling foursub-systems (picture 1):

Picture 1: Systemic representation of the framework1. The “Human system” made up of the driver and his psychological, cognitive andsocio-professional characteristics. He is an “actor and subject” of the observations,which allow invariants and variability of behaviours.2. The “technical system” made up of the truck and its physical and technologicalcharacteristics. It is the “essential tool” for the driving activity and becomes thedriver’s home in case of long-distance transport. The driver’s behaviours manifestthemselves through its different switches and controls (accelerator, brake, gear, etc.).3. The “assistances systems” mainly made up of integrated algorithms into theVehicle Electronic Control Unit (VECU) and a Human-Machine Interface (HMI)between the driver and the system. Simple informative systems at the beginning, theycan be reformulated as more or less passive/active assistants, even as a co-pilot.4. The “road system” not only made up of varied substructures and many differentusers, but also of several uncertain factors (as traffic; weather, exceptional events,etc.). This is a complex dynamic environment evolving in a constant way, in whichthe whole previous systems evolve.Furthermore, (1) there are many interactions between these different systems, (2) thedriver’s behaviours mainly depend on these interactions and (3) we think that thereflect of these interactions can be evaluated through the assessment of the driverworkload on the one hand, and on the other hand by recording the driver’s actionsduring the driving activity. Therefore, our approach tries to take these differentdimensions into account.

2.1. Aims of the researchThe main goal of this study is to find a model of truck drivers’ behaviour with respectto a rational driving, and build a typology of these behaviours, which could beexploitable by the system. To this end several workshops according to the features ofthe driving activity have been kept:- A socio-economical approach of haulage of goods,

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- A study about drivers’ representations and conative factors linked to the job andthe truck driving,

- The translation of a rational driving in objective driving criteria by use ofpsychological and ergonomics methods,

- The definition of the drivers’ behaviours according to the previous criteria,- The behavioural modelling of truck drivers by the identification of different

driving styles and their variability,- The assessment of the new Human-Machine Interface,- The evaluation of a tool intended for the assessment of the drivers’ workload

caused by the driving activity: to test its sensitivity, its reliability concerning thetruck driving tasks and its understanding by a truck drivers population (picture 2).

Picture 2: Workload driving index (NASA TLX)The tool we used to assess the driver’s workload is a French adaptation (Maincent,2001) of the NASA TLX (Hart and Staveland, 1987). It has been adapted to thetruck driving tasks. This multi-dimensional subjective procedure provides anoverall workload score and a weighted rating fro each factor. It takes into accountsix factors, which can influence the subjective experience of workload: threefactors arise from the task itself – mental, physical and temporal demands- andthree factors arise from the interactions between driver and task – effort,frustration and own performance.

The modelling will provide the engineers with objective behavioural criteria tointegrate into the Vehicle Electronic Control Unit in order to identify the “bad”behaviours, which the system could correct and the “good” ones, which will bepreserved. The workload index will be used to investigate the truck driver’s workloadcaused by the driving tasks with new systems of assistance. The assessment of theHuman-Machine Interface will be turned towards its efficiency, its acceptance by thedrivers, its easiness to use and the absence of perturbation while driving.

2.2. FrameworkAs we said previously, driving a vehicle requires varied mechanisms, which are notonly based on cognitive skills but also on knowledge and meta-knowledge as well ason the driver’s expertise. Thus, because of this diversity the cognitive analysis of thedriving activity, from a psychological as well as an ergonomic point of view, needsthe setting of a multidimensional experimental device. Moreover, in order to provide a

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necessary ecological validity to the results, it must be carried out in an operationalsituation or in a situation that could be the nearest possible one that the usual truckdriving activity.At least, to answer the particular targets of the study in this scenario of rationaldriving, we have identified some classes of relevant situations in the context of whichour observations will be analysed such as roundabouts, crossroads with traffic lights,changes of incline (particularly in downward slopes) and town crossings.

3. MethodThe observation method is based on a psychological and ergonomic analysis of thetruck driving activity, in a real driving situation and on different types of roads in theRhône-Alpes district (France).

3.1. Experimental plan33 drivers took part in the study. 23 “experts” are professional drivers of theDarfeuille and Venditelli firms (St Priest-France); 7 “novices” driving few kilometresa year, they are employees of Renault Trucks; and 3 “witnesses” are rational drivingtrainers of Renault Trucks.The experimental itinerary has been established so that they are composed of all theclasses of situation that we had first identified, and the road infrastructures are enoughvaried to represent the major part of the road transport forms, except urban andsuburban areas and messenger services. This route has been divided in 2 sections:

1 The route “A” replies the situations with which the regional transports driversare confronted. It is a varied and difficult road mainly made up of highwaysand urban areas (92 km – 57,17 Miles) with a difference in height of 500 m,10 town crossings, 20 traffic lights, 16 roundabouts and 17 bends.

2 The route “B”, replies the situations with which the long distance (nationaland international transport) drivers are confronted. It is an easier road mainlymade up of motorways (78 km – 48,48 Miles) with a difference in height of400 m, 1 suburban area crossing, 5 traffic lights, 1 roundabout and 1 bend.

The tractor semi-trailer combination consists of a road tractor “Renault TrucksPremium 420 dci” coupled to a semi-trailer (weight rating = 38 T).The drivers were asked to drive freely as they used to do, without any specific tasks.

3.2. Experimental protocolRegarding to the modelling of driving behaviours, we have postulated that (1) thedriving activity is mainly composed of motor and cognitive abilities, which are theconcern of procedural memory and therefore not easily expressible in a declarativeform, and (2) the driver’s automatic behaviours manifest themselves through thevehicle controls. So, we have chosen to record in real time, the driver’s actions on thecontrols. The experimental procedure has been conducted according to the followingmodalities:1. The vehicle data are directly recorded from the VECU with a laptop and specific

software, the “VECTOR CANalyser”. Then they are handled with the help ofsecond software that we developed especially for this study, the “DriverBehaviour Analysis”.

2. During the driving, the experimenter points out the road environment events(traffic, weather, etc.) and the particular driver’s behaviours (use of cellular

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phone, horn, etc.) using a grid and another specific software the “Actopalm –Actogram”.

3. In addition, at the end of each trip, the drivers fill in a workload index to assesstheir driving workload.

4. At the end of the journey, the drivers fill in varied questionnaires and tests:- About the cognitive and conative aspects of truck driving (representations,

motivation, etc.),- To evaluate their knowledge and meta-knowledge especially regarding

rational driving,- To estimate the use and acceptation of driving assistances already set up in

the truck such as the force feed-back in the accelerator pedal,- To investigate their cognitive and personality styles, the Bortner test, the

STROOP test, the Eysenck Personality Inventory and a locus of control.Each driver took the same experimental procedure driving the same truck, and thecomplete procedure made about five hours by driver. In the order to drive in the sameconditions in term of traffic, the trips have been scheduled in the middle of the day,once a week, except during school holidays. For these reason the experimental partlasted more than a whole year.

4. First resultsHereafter, are shown the quantitative results of the descriptive analysis of the drivingactivity in term of workload and rational driving (consumption and speed). Thequalitative results in term of behavioural modelling and validation of the Human-Machine Interface will be dealt with in a next article.Our statistical analysis have been made with “Statistica by Statsoft” and we used non-parametric analysis methods, especially the Mann Whitney U test, a n d theCorrelations Spearman.

4.1. Drivers’ workload analysesIndependent variablesRoute “A” = a difficult route made up of highways and urban areas.Route “B” = an easy road essentially made up of motorways.Dependent variables6 Factors of workload: Mental demand, physical demand, temporal demand, effort,frustration and own performance + 1 overall score of workload.Sample: 33 truck drivers.The statistical analyses about the Workload Driving Index show significantdifferences – according to the followed route. On the one hand, the “overall score A”is higher than the “overall score B” (p < ,01) and on the other hand, the “mentaldemand A”, “physical demand A”, “effort A” and “frustration A” are higher than the“mental demand B”, “physical demand B”, “effort B” and “frustration B” (all p <,02). In addition, the factor “mental demand” is the highest factor on the route A. Atlast, there is no significant differences between the factors “temporal demand A andB”, and “own performance A and B” (picture 3).

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Picture 3: Analysis of workload factors according to the experimental route

Thus, on the whole, the drivers’ experienced workload is mainly higher to drive atruck along a difficult road mainly made up of highways and urban areas with a lot ofvaried classes of situation (roundabouts, traffic lights, bends, etc.) than to drive alongan easy road mainly made up of motorways and a poor road environment. In addition,it seems that the workload caused by the driving activity is essentially a mentalworkload, especially to drive along a difficult road.As regards the factor “temporal demand”, the experimental situation was notcomposed of any temporal demand whatever the route; so, we did not expected anydifference between both situations.As regards the “own performance”, this factor is the higher factor for the route “B”and the second factor for the route “A” after the “mental demand”. Regarding theparticular constraints of the truck driving activity in term of security and consumption,we can suppose that the drivers attach same importance to their own performancewhatever the road environment.At last, there are significant correlations (p < ,05) between the whole factors and theoverall score according to the experimental route.Thus, the test is quite sensitive to distinguish two usual driving situations without anyparticular constraints, which are only different as far as road infrastructures areconcerned. This workload-driving index seems to be a relevant method to analysedrivers' workload caused by driving activity in a real world environment, providingdiagnostic information about sources of loading.

4.2. Inter-individual variability about fuel consumption and speedIndependent variablesRoute “A” = a difficult route made up of highways and urban areas.Route “B” = an easy road essentially made up of motorways.Dependent variablesFuel consumption + Speed (presented in gaps/drivers' average)Sample: 33 truck drivers.As we specified before, rational driving can be considered as a compromise betweenspeed and fuel consumption. Then it seemed necessary to carry out a first descriptiveanalysis of the data, not only according to fuel consumption but to speed as well.

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Comparison of consumption and speed, by driver, on the route A (highways & urban areas)

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Comparison of consumption and speed, by driver, on the route A (highways & urban areas)

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Comparison of consumption and speed, by driver, on the route A (highways & urban areas)

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Picture 4: Inter-individual variability about consumption and speed (route A)

These first analyses, which were only descriptive, pointed out a great inter-individualvariability about fuel consumption and speed too (picture 4). In fact, we notice animportant scatter, particularly concerning fuel consumption, according to the drivers’driving styles whatever the experimental route. The picture 4 shows the best drivingbehaviours with a low consumption without waste of time on the one hand, and on theother hand, the worst driving behaviours with a high consumption and a low speed.The existence of this variability is not surprising since it is the cause of the project,nevertheless we did not expect such an important scatter. For this reason, engineersthink they could decrease the fuel costs of a trucks fleet from 4 to 10% with theirassistance system, whatever the driver.In addition, we pointed out significant correlations between “consumption A” and“overall score of workload A” and between “consumption B” and “overall score ofworkload B”

4.3. Dissociation consumption/speedNuage de Points : Vit A par Conso A

Conso A = 50,280 + ,06692 * VitACorrélation: r = ,05912

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Picture 5: Dissociation consumption/speed on the experimental route A (n=33)

At last, among interesting results for the engineers, the giving prominence to thedissociation consumption/speed held on all our attention. In fact, no correlations arepointed out between “consumption A” and “speed A” as well as between“consumption B” and “speed B” (Picture 5). This dissociation, which is observed forthe route “A” as well as for the route “B”, really proves that a driver or a technicalsystem can quite decrease the truck fuel consumption without wasting time however.So we corroborate the principles of rational driving: the trucks consumption of a fleet

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can be balanced without, as far as we know, increasing more or less delays fordelivery and thus without damaging the productivity of trucks, their security or thedriving comfort.

ConclusionThe statistical analysis of the recordings and the report of the progress of theobservations as they advanced have already allowed to sustain the global project in alongitudinal way, particularly as far behavioural data are concerned to enrich existingmodels for simulation tools.The needs as regards presentation and data processing coming from the vehicleelectronic calculator have led the writing of a specific computer program underMatlab: the Driver Behaviour Analysis. This simple and user-friendly tool can beeasily used without any particular knowledge from any file, which has been recordedfrom a Vehicle Electronic Control Unit via the “Vector CANalyser”. It could be a veryuseful help for varied experimental procedures and driving trainings.Regarding to the workload study, the used method proved itself quite relevant in thecontext of the truck driving activity. It appears to be rather sensible to providediagnostic information about subjective workload of drivers in a real situation and todistinguish two kinds of driving situations. Moreover, the whole drivers who took partin the study have quickly understood the method. This test could be then used to testnew assistance systems, which are likely to have an action on the workload for thepurposes of conception or validation.As far as the validation of the Human-Machine Interface is concerned, thedevelopment and the testing of the system are in progress on the dynamic drivingsimulator of Renault Trucks (The SCOOP). An experimental protocol is in thepipeline to test the system with experimented drivers.By conceiving a rational driving assistance, the manufacturer hopes to decrease thetrucks consumption by 7%, whoever driver and whatever the driving style is like.Moreover, considering the actual international context as regards the increase ofpetrol costs, the trucks fuel consumption should become a real problem for the firmsof transport. Consequently, a system, which could be able to decrease thisconsumption, whatever the driver, seems to be an interesting solution for the future aspart of a long-term development.However, this action (of development) must not be isolated and as far as drivers’behaviours are implicated, we can easily assume that trainings, awareness campaigns,etc. will also play an important part in this precise context.Furthermore, we think that drivers, because of their behaviours, must leave in thecentre of the manufacturers’ researches as regards the vehicles consumption,especially within the framework of partnerships between human sciences andengineering sciences.

ReferencesAmalberti, R. (1996).La conduite des systèmes à risques. Paris: PressesUniversitaires de France.Hart, S.G., Staveland, L.E. (1987). Development of NASA-TLX (Task Load index):Results of empirical and theoretical research. In: P.A. Hancock and N. Meshkati(Eds.), Human Mental Workload. Amsterdam : Elsevier.Hillmeyer, F. (2004). Extrait du rapport pour la mission parlementaire « TransportRoutier de Marchandises ».

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Maincent, A. (2001). De la pertinence de l’utilisation du NASA TLX associé à desindicateurs physiologiques dans l’évaluation de la charge de travail de tâches àcomposante mentale. Mémoire du DEA Dimensions Cognitives et Modélisation.LEACM, Institut de Psychologie, Université Lumière Lyon II.Perruchet, P. (1988). Les automatismes cognitifs, Liège: Mardaga.Pinchon, P. (2003). Editorial. Revue de l’Institut Français du Pétrole, vol. 58, n° 01, p3-4.Sperandio, J.P., (1995). Ergonomie Cognitive. Psychologie Française, 40-1.Stich, P. (2004). Copilote à bord ! Transport & Technologies, n° 46 – mars 2004, p.80-81.

Keywords:Truck driver’s behaviour, rational driving, driving assistance system, mentalworkload.