CONTEXT-BASED INTELLIGENT EDUCATIONAL SYSTEM FOR

CAR DRIVERS

Juliette Brezillon, Patrick Brezillon, Thierry Artieres

LIP6, University Paris 6, 104 avenue du President Kennedy, 75016, Paris, France

Charles Tijus

Cognition et Usages, University Paris 8, 2 rue de Liberte, 93526 SaintDenis Cedex 02, France

Keywords:

Driver modeling, Cognitive Sciences, Machine Learning, Context, Situation Awareness.

Abstract:

Although a driving licence concludes training, such an initial is insufﬁcient because new drivers do not know

how to contextualize the learned procedures into effective practices. Our goal is to improve the drivers’

situation awareness, in which the drivers perceive the environment’s events and the projection of their status

in a close future. To achieve this goal, we design an educational system for the drivers, which help them to

become aware of their driving errors. This educational system aims to identify and correct drivers’ drawbacks.

In this paper, we discuss the reasons for associating two approaches: a local approach (resulting from cognitive

sciences) and a global approach (resulting from machine learning), and we show the key role that context plays

in the driving activity.

1 INTRODUCTION

Car driving is a complex activity that needs practical

experiments to be safe. Initial training required a driv-

ing license that appears insufﬁcient because young

drivers do not know how to contextualize the learned

procedures in effective practices. A reason is that

learners are always with a car instructor that moni-

tors (and corrects) their actions. Thus learners rely on

this support and do not develop an efﬁcient awareness

on the driving task.

As a consequence, novice drivers are propor-

tionally more involved in accident than experienced

drivers. Inexperience concerns several aspects of

drivers’ cognition, but the main factor of novice

drivers’ errors is an inadequate mental representation

of the driving situation. The learner does not consider

the relevant contextual cues related to his driving task.

Indeed, driver’s decision making is not based on the

objective state of the world, but on a mental model

of the driving task and the conditions to accomplish

this task. This mental model is a circumstantial repre-

sentation of (Richard, 1990) built in a working mem-

ory from perceptive information extracted in the road

scene, and form permanent knowledge stored in the

long-term memory.

This representation provides a meaningful and

self-oriented interpretation of the reality, including

anticipation of potential evolutions in the current driv-

ing situation. From this point of view, this corre-

sponds to the driver’s Situation Awareness, (Endsley,

1995): ”The perception of the elements in the envi-

ronment within a volume of time and space, the com-

prehension of their meaning, and the projection of

their status in the near future”. Moreover, this men-

tal representation is ”action-oriented” (i.e. the driver

is an actor not a witness). Mental representations

are a key element of the driver’s cognition. An er-

roneous representation means, potentially, decision-

making errors and unsafe driving actions. Hereafter,

the paper is organized as follow: the ﬁrst part aims

to explain the previous works our project is based

on. The following part describes the global and lo-

cal methods that are used in this project. The third

part gives our viewpoint about the context. The last

section presents the ﬁrst results of our project, which

are an errors-based drivers’ typology and the choice

of a case study.

403

Brezillon J., Brezillon P., Artieres T. and Tijus C. (2007).

CONTEXT-BASED INTELLIGENT EDUCATIONAL SYSTEM FOR CAR DRIVERS.

In Proceedings of the Ninth International Conference on Enterprise Information Systems - AIDSS, pages 403-406

DOI: 10.5220/0002366204030406

 SciTePress

2 PREVIOUS WORKS

2.1 Gadget Project

The GADGET, acronym for ”Guarding Automobile

Drivers through Guidance Education and Technol-

ogy”), project (Siegrist, 1999) was a European project

about safety road. It aims to assess trafﬁc safety mea-

sures on driver behavior; analyze the inﬂuence of in-

car safety devices, various road environments, educa-

tion and training programmes, safety campaigns, and

legal measures (including enforcement) on driver be-

havior. This project stars from the work of (Van der

Molen, Btticher, 1988) who split driving tasks and

driving behavior into three hierarchic levels: Strategic

level, Maneuvering level or Tactical level and Con-

trol or operational level. In the GADGET matrix,

later also referred to as the GDE matrix, a fourth level

was, however, added above the other three, the Polit-

ical level. We make the assumption in our work that

GADGET methodology can be revisited at the light of

the notion of context. We make the assumption that

all the variables in the GAGDET matrix can be used

to describe the context of the driving activity.

2.2 Other Study

Another work on which we based ourselves on is the

work of (Young, 2006) which shows that it’s better to

learn from errors than from successes. The study ex-

amines whether error exposure training can enhance

adaptive performance. Fifty-nine experienced ﬁre-

ﬁghters undergoing training for incident command

participated in the study. War stories were developed

based on real events to illustrate successful and un-

successful incident command decisions. Two training

methodologies were compared and evaluated. One

group was trained using case studies that depicted in-

cidents containing errors of management with severe

consequences in ﬁre-ﬁghting outcomes (error-story

training) while a second group was exposed to the

same set of case studies except that the case studies

depicted the incidents being managed without errors

and their consequences (errorless-story training). The

results provide some support for the hypothesis that it

is better to learn from other people’s errors than from

their successes. That’s why we based our driver’s ty-

pology on driving’s errors.

3 GLOBAL AND LOCAL

METHODS

The main idea of the project is to associate global

method resulting from machine learning and local

methods resulting from cognitive sciences. The idea

to associate global and local methods comes from the

limitations of each method. Indeed, the local method

performed very well to describe a driver’s behavior at

the highest levels (in the most general way) but can

not describe a driver’s behavior on the lower levels

since it must take into account too much variables.

We have the opposite problem in the global methods.

They analyse very poor data (from the lower levels)

and infer some more general information. But they

are limited and can’t describe drivers’ behavior at the

upper levels. The global methods can only give gen-

eral information on the driver’s behavior in their driv-

ing task, whereas there is a high individual variabil-

ity in this kind of study since each driver is a par-

ticular case who acts with a set of contextual cues

highly personal that differ one from all other. More-

over, one driver can present very different behaviors

for the same driving task since the contexts in which

he is doing this task can be very different. Thus, a

global method constructed from every day life’s data

allows establishing a driver classiﬁcation in reality,

which has to be completed by a local method. In this

way, we hope to be able to describe a driver at all the

levels in the same time. The association of the two

approaches allows modeling the driver at all the lev-

els of the matrix proposed in the GADGET method-

ology. Thus we solve some problem found in litera-

ture which are the facts that some studies analyze the

driver at one level at the time; for example they stud-

ies at the tactical level. But each level depends on

highest levels, what is a limit of the other studies.

4 FIRST RESULTS

4.1 Driver’s Risky Behaviors Typology

Our driver’s typology, based on driver’s risky behav-

ior, is resulting from a questionnaire. This question-

naire has been made by taking all the variables in the

GADGET matrix, completing by others we thought

was missing, and by organizing them not by layers

as it was usual to do, but by type. The question-

naire was then done to implements 61 variables (is-

sues from GADGET and complete by ourselves). It

has 162 questions, there are thus some reformulations

in the questions to stage the skew of the morality of

ICEIS 2007 - International Conference on Enterprise Information Systems

404

the driver and to determine if it lies or not. To stage

this skew, we also guided the questions about the facts

while trying to ask for the least most often possible

the opinion of the driver about his own behavior. The

results show here are issues from the ﬁrst 214 partic-

ipants to that questionnaire. From this data, we ex-

tract 16 classes, by doing a principal composant anal-

ysis to reduce the 61 variables to 3, and we classify

these new data thanks to the agglomerative methods

in 16 classes. We then identify for each class variables

which describe the class; they are variable which has

a speciﬁc value in that class and another value in the

others classes. We after determine for each class the

speciﬁc variables which are relative to risky features

and behaviors. Here are these variables for each class:

1: driver does not think of managing the unfore-

seen situations and do not plan its way

2: driver feels to develop by his driving

3: driver already was under the effect of tiredness

and alcohol in his driving

4: driver already was under the effect of alcohol

5: driver thinks of having basic competences to

drive, missing automatism, driving in bad mood, feels

to develop by his driving, and drive to decompress

6: driver has tendency to risks and is accustomed

to taking risks

7: driver has tendency to risks and is accustomed

to taking risks, does not always control the trajectory

of its vehicle, does not always put its indicators, miss

automatism, drive in bad mood, does not always make

good prediction in the evolution of the driving situa-

tion and feels to develop by his driving

8: driver doesn’t thinks to have basic competences

to drive, does not always put its indicators and does

not always control the trajectory of its vehicle

9: driver already was under the effect of alcohol

and drugs, and has already have an aggressive behav-

ior in his driving

10: driver does not always make good prediction

in the evolution of the driving situation

11: driver thinks he has a driving style and feels

to develop by his driving

12: driver does not think of managing the unfore-

seen situations and does not always make good pre-

diction in the evolution of the driving situation

13: driver does not consider his car like a purely

functional instrument

14: driver is an amateur of races of cars, at search

of strong feelings, and already has an aggressive be-

havior in his driving

15: driver doesn’t thinks to have basic compe-

tences to drive; he thinks he misses automatisms and

does not always make good prediction in the evolu-

tion of the driving situation

16: the driver do not think of managing the un-

foreseen situations, doesn’t thinks to have basic com-

petences to drive, doesn’t drive if there is fog, or snow,

or glaze, if the trafﬁc is dense, already was under the

effect of alcohol and drugs, does not always make

good prediction in the evolution of the driving situ-

ation, thinks he has a driving style, has already have

an aggressive behavior in his driving, does not see the

road as a social space and is not sensitive to social

pressure.

This typology of drivers’ errors is the ﬁrst step of

our work. We aim further to identify a driver’s behav-

ior in this typology to analyses his drawbacks and to

help him to improve his situation awareness by cor-

recting this drawbacks thanks to adapted scenarios on

simulation.

4.2 The Case Study of Our

Experimentation

We take a real trafﬁc situation -a simple crossroad-

and try to analyze all the driving situations that can

happen. We assume only two cars arriving to the

crossroad. We select the viewpoint of the driver of

Figure 1: Real situation driving.

car A (coming from the bottom), and analyze all the

options, ﬁrst, according from where is coming the car

B (from the left, the right or in front of car A), and sec-

ond, according to the movement of the two cars (turn

left, straight ahead, or turn right) at the crossroad. We

model all the behaviors by contextual graphs. In the

retained trafﬁc situation, each road has a ”Yield” sign.

This means that no priority has been deﬁned and the

rule is ”priority to the car on the right side”. This

crossroad can leads to 27 initial trafﬁc situations, ac-

cording from where is coming car B and where are

going the two cars. After deﬁning our real driving sit-

uation, we model with contextual graphs: topology of

the crossroad, road sign position, theoretical behavior

of the drivers, possible behaviors of the drivers.

CONTEXT-BASED INTELLIGENT EDUCATIONAL SYSTEM FOR CAR DRIVERS

405

The topology of the road show how the law de-

cides that a crossroad needs a yield road sign. This

takes into account the speciﬁcity of a crossroad, and

the visibility to evaluate that needs. The road sign

position means that once we have established that a

crossroad has the need of a yield sign, we have to de-

termine the position where it would be. This model

shows that the position will be not the same in a city

or in countryside. Since the chosen crossroad has no

special priority, the law deﬁnes the ’theoretical” be-

havior as ”to yield the emerging passage to the vehi-

cles of right-hand side, by having a special vigilance

and a deceleration adapted to the announced danger.”

There are some restrictions with this panel: the trams

have right of way and if the topology of the cross-

roads obliges it, a special panel added to the ﬁrst deﬁ-

nite the priority. The theoretical behavior established

from the law texts is to check that the roadway to

cross is free, to circulate with a moderate speed es-

pecially if the conditions of visibility are worse, in

the event of need, to announce our approach, must

engage in an intersection only if our vehicle does not

risk to be immobilized in the crossroad area and to an-

ticipate the passage of the vehicles circulating on the

other ways. There are two successive parts: the anal-

ysis of the situation and the process of the decision

making itself. Possible behaviors have been analyzed

start from what can happen concretely in that cross-

road that is not planned by the law. First, the car’s

driver, which has not the priority, does not stop and

enters the crossroad, because for instance, the car’s

driver thinks that he has time to pass before the other

car, or he didn’t see it. Then, he can realize that he’s

making a mistake and decides to stop in the middle

of the crossroad. The other car attempts to avoid him.

Moreover, the two car’s drivers can break down. If

a car’s driver breakdown, the other car’s driver will

have to wait until the other starts again and leave the

crossroad, or decides to overtake it. If the driver over-

takes, the ﬁrst car can start again and realize the other

car is in front of him and try to avoid him. Or maybe,

the other car’s driver was not attentive and didn’t see

that the car driver breaks down, thus he will has to re-

act at the time he will realize the problem, and he has

still some. We determine, thanks to this case study,

the drawbacks of the driver behavior. We have sev-

eral possible scenarios on this situation and each is

linked to class of our errors-based driver’s typology.

For example, the driver who is not attentive (and who

belongs to the class 12 on our typology) would make

the scenario in which he would not see the other car

on the crossroad. With the correlation, we are able

by making pass this speciﬁc driving situation to any

driver to identify his drawbacks and his errors in his

driving thanks to our typology and we would be able

to help any driver to improve his situation awareness.

5 CONCLUSION

Driver modeling is an important domain that interests

a number of administrations (for a uniform road se-

curity in European countries, for the police for inter-

preting correctly drivers’ behaviors, for associations

wishing introducing some changes. etc.). This is also

an interesting ﬁeld of investigation for AI researchers.

Our contribution brings at least three new insights on

this hot topic. First, we propose ”driver’s based” clas-

siﬁcation of drivers and not an arbitrary classiﬁcation.

Second, we propose an open modeling in the sense

that it is possible to incrementally acquire new behav-

iors of drivers. Third, we use good and bad practices

for driver’s self-learning, bad practices being mainly

used by the system for identifying what is doing a

given driver, and how to help him to correct his be-

havior.

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