TRANSPORTATIONBASIC STUDY ON TAILORMADE BRAKINGSUPPORT SYSTEM
Toshiya HIROSE, M.S.

Toichi SAWADA, Prof.

Yasuhei OGUCHI, Prof.

Shibaura Institute of Technology
Saitama, Japan

Shibaura Institute of Technology
Saitama, Japan

Shibaura Institute of Technology
Saitama, Japan(Received July 26, 2004)
It is desirable for driving support systems to improve the safety of driver vehicle systems, and at the same time to have a performance that
does not make individual drivers feel uncomfortable. Since human beings have various control characteristics, any system that supports driving under fixed conditions without taking such characteristics into consideration cannot be a driving support system in the true sense. The authors believethat only those systems that reflect the characteristics of individual drivers improve safety and pave the way for their widespread use, and proposal
Tailormade Driving Support System (TDSS) in IATSS RESEARCH Vol. 28 No. 1. This TDSS is composed of three systems that support braking,steering and accelerating, and it gives assistance fitted to individual drivers with a driver model that uses a neural network.
This research reviewed the construction of models of a Tailormade Braking Support System (TBSS) for braking to stop vehicles and the evaluation of drivers. As a result, the following conclusions were drawn. (1) Braking factors were found to change in the period from the start of braking to
stopping; (2) Changes in braking factors can be logically incorporated into the control elements of braking support system; (3) Readymade Driver
Model is effective as a model to be incorporated into the base system of TBSS; (4) Tailormade Driver Model built on Neural Network is effective as amain model to construct TBSS; (5) As for TBSS, both subjective and objective ratings on the timing and magnitude of braking are favorable, and its
safety and sense of security are improved.
Key Words: Tailormade, Driving Support System, Braking, Neural Network, Driver Model

1. INTRODUCTION
Driving support systems have great effects on improvement of pre-crash safety and reduction in driving
loads1-7. However, even a system that is expected to yield
effects cannot come into wide use, if drivers as users feel
uncomfortable with, and puzzled about it. Human beings
have a variety of personalities, and the same goes for driving. Any system that defines characteristics of humanbeings in the same manner without taking such variety
into consideration cannot be a true driving support system. As for the construction of a system that reflects
characteristics of individual drivers, the authors reported
a framework of a Tailormade Driving Support System(TDSS) 8. The effect of this system on drivers is to allow them to feel quite normal when they use the system.
This system first uses a Readymade Driving Support System as a base system that has the general characteristics
of drivers incorporated into it, and builds TDSS by brushing up a Readymade system, while reflecting the characteristics of daily driving by individual drivers. For
information, TDSS is composed of three systems that support braking, steering and accelerating. TDSS does notnecessarily cope with all scenes of traffic, but it automatically restrains even personal characteristics in situations
that involve safety-impairing actions, emergency braking,68 • IATSS RESEARCH Vol.28 No.2, 2004

etc., and the machine ensures safety. Therefore, this research covers braking under normal operating conditions.
This research focuses on braking, and evaluates a
Tailormade Braking Support System (TBSS) that is built
on a neural network (NN). This system first analyzed
braking factors of drivers through experiments, developeda Readymade Driver Model from clustering of major control elements, and constructed TBSS that reflects the characteristics of individual drivers. In closing, the research
verified the effect of TBSS on drivers through objective
and subjective ratings.

2. BRAKING SUPPORT SYSTEM THAT USES
BRAKING FACTORS OF DRIVERS
2.1

Braking factors of drivers in braking
For construction of a braking support system, determination of control elements has important implications. For example, control elements in case of theapproach of a leading vehicle include headway distance,
relative velocity, relative deceleration, etc. To date, theseelements have been set by trial and error, and safe andworry-free control has been studied. Here, the input information used by drivers for braking is defined as braking factors, and the frequency of the input information

BASIC STUDY ON TAILORMADE BRAKING SUPPORT SYSTEM

T. HIROSE, T. SAWADA, Y. OGUCHI

2.2

Headway distancex1

Braking effort
Y

Relative velocity
x2

Analysis of braking factors
x1

x3
Relative
deceleration

W1
W2

x2

y

W3
x3

Fig. 1 Braking factors with NN

handled by drivers concerning headway distance, relative
velocity and relative deceleration were prioritized. Thesepriorities are reflected in control elements, aiming at safe
control and resolution of uneasiness felt by drivers. For
example, relative deceleration of higher priority is used
as a braking factor in the first part of braking, relative velocity in the middle part, and the headway distance in thelatter part. In this way, braking factors are changed withtime in proportion to the priorities of braking factors.
Braking factors of drivers were analyzed by a factor analysis of NN9, 10. The factor analysis is a method
to find the degree of the effect of each input on output
by checking a magnitude of connection weight that is
identified by NN's learning11. Fig. 1 shows the relationship between the input and output in a neuron model.
Here, xi is an input into a neuron, y is an output from aneuron, f is a sigmoid function, wi is a connection weight,
and b is the bias of the neuron. The calculation performedin the neuron model is shown in Expression (1). Whenthe magnitude of each input is calculated by the ratio to
all inputs, the level of influence Fi of ith input xi on output can be expressed by Expression (2). The levels of
influence of inputs including headway distance, relative
velocity and relative deceleration on a braking effort asan output are calculated by applying the level of influence calculated by one neuron model to the neural network as a whole. Relative deceleration was used because
the case where a leading vehicle decelerates and one's
own vehicle approaches it was taken into consideration.

 n
y = f ∑ ( wi ⋅ xi + bi / n )  i =1Fi = wi ⋅ xi + bi / n(1)n∑ w ⋅x +b /n
i

i =1

i

i

(2)

Method of experiment on braking
A driving simulator12 that can ensure the safety ofsubjects and easily change experimental conditions wasused for the experiment.
The experiment was conducted by setting the headway distance and deceleration of a leading vehicle as shownin Table 1. The driver drives at a speed of 27.8 m/s (100km/h) on a highway reproduced on the screen of the driving simulator. During the drive, a leading vehicle is visible on the screen, and the driver drives behind the leading
vehicle with a specified headway distance. The leading vehicle brakes after driving over a certain distance and simulator driver's vehicle approaches the leading vehicle. Then,the driver conducts his or her usual braking action. This
experiment measures the headway distance, relative velocity, relative deceleration, and deceleration of the simulator
driver's vehicle.
Table 1 Experimental parameters
Velocity (m/s)

27.8

Headway distance (m)20, 40, 60, 80

Deceleration of the leading vehicle (m/s2)

1, 2, 3, 4, 5

2.3

Analyses of braking factors
The precision of the driver model built on NN wasverified by braking simulation of non-learnt data. Fig. 2
shows the simulation results. With these results, a factor analysis of this NN is conducted as NN brakes likethe driver, and the priorities of braking factors are determined. Under the conditions of a 40 m headway distanceand a 2 m/s2 deceleration of the leading vehicle as shownin Fig. 2, a difference is seen in the latter part of braking. This seems to be attributable to variations in teacher
signals as safety is ensured in the first part of braking andthe degrees of freedom in the driver's braking are large
in the latter part of braking. However, this latter part ofbraking does not give any sense of discomfort to thedriver because it is in a state of low speed.
Fig. 3 shows the factor analysis of NN after learning, and the priorities of braking factors of the driver. Fig.
3 shows a tendency of braking factors that relative deceleration became larger in first part, relative velocity
became larger in the middle part, and headway distancebecame larger in the latter part. So, the factor of relative deceleration tends to become large at the time of thestart of braking, and then the factor of relative velocity
tends to become large. The shorter the headway distanceis, the larger the factor of headway distance becomes.
The same tendency was found under other experimental
IATSS RESEARCH Vol.28 No.2, 2004 •69

TRANSPORTATION

120

Braking effort (N)DriverDriver

NN

NN

80

40
Headway distance 40 (m)Deceleration of theleading vehicle 2 (m/s2)
0
0

10

Headway distance 60 (m)Deceleration of theleading vehicle 3 (m/s2)

15

20

0

Time (s)

10

15

20

Time (s)Fig. 2 Simulation of the driver’s braking (Subject a)First

Middleconditions and by other subjects. This shows that drivers
start braking in the following sequence of braking factors,i.e., relative deceleration, relative velocity and headway distance due to the approach of a leading vehicle.

LatterBraking factor (%)

100

Relative
80 deceleration
60 Relative
velocity40

2.4

20 Headway
distance00510Time (s)1520(a) Headway distance 40 (m)Deceleration of the leading vehicle 2 (m/s2)

First

Braking factor (%)

100

MiddleLatterRelative deceleration

80 Relative
velocity60
40 Headway
distance
20
0

0

3

6

9

12

15

Time (s)(b) Headway distance 60 (m)Deceleration of the leading vehicle 3 (m/s2)

Fig. 3 Braking factors about the headway distance,
the relative velocity and the relative deceleration(Subject a)

70 • IATSS RESEARCH Vol.28 No.2, 2004

Control elements of braking support system
A braking support system sets the values of control elements based on the driver model and brakes in response to a driver. Several techniques are reported tobuild the Driver Model including NN13-15, Fuzzy model16,
or Fuzzy Neural Network17 as a combination of NN and
Fuzzy model, and a technique to add physical quantities
such as velocity and acceleration together with quantitative amounts such as the experience and personality ofthe driver up to the inputs for modeling18. Under thisstudy, the Braking Support System is built by using clustering and NN. A Braking support system of clustering
was constructed by two ranges, i.e., one is to brake before recognition of safety (braking range) and the otheris to brake after recognition of safety (following range).
The braking support system selected the values that represent control elements in each range from the prioritiesof braking factors. The control elements in the braking
range are defined as a magnitude of maximum deceleration and rate of change in deceleration. Next, the control element that decides a change from the braking range
to the following range is defined as a relative velocity.
The control element in the following range is defined asa headway distance. For the timing of braking, a headway distance is used because only the visual informationof the driver is applicable.

BASIC STUDY ON TAILORMADE BRAKING SUPPORT SYSTEM

T. HIROSE, T. SAWADA, Y. OGUCHI

Fig. 4 shows a conceptual diagram of the braking
support system. The braking support system is composedof the braking range and the following range. Braking
in the braking range is divided into Braking Range AA
and Braking Range BB. Braking in Braking Range BB
represents the case where additional braking is requiredfor compensation after the occurrence of maximum deceleration. Therefore, if a driver fully brakes in Braking
Range AA, the system changes to the following range
without causing Braking Range BB. The Readymade
Driver Model classifies the control elements in each range
by clustering, and sets the values of central tendency ofgroup in the braking support system. For example, characteristics of start of braking are: I) headway distance atthe time of start of braking; II) rate of change in deceleration until the vehicle reaches maximum deceleration;and III) maximum deceleration.

Deceleration (m/s2)

Brakingrange

Followingrange

Relative
velocityAA
Headway
distance

Start of thebrakingII

I

Braking Range BBt′1
V ′ = ∫ ( β − αt ′)dt ′ = − αt ′2 + β t ′ + V0′
0
2

(4)

α : The gradient of deceleration after the change fromthe maximum deceleration to Braking Range BB;β : The maximum deceleration;V´0 : The velocity at the time of change to Braking
Range BB;t´ : The time after the change to Braking Range BB.
Following Ranget ′′1V ′′ = ∫ ( β ′ − αt ′′)dt ′′ = − αt ′′ 2 + β ′t ′′ + V0′′
0
2

(5)

The following range has two cases, i.e., the casewhere Braking Range BB exists and the case where Braking Range BB does not exist.

BB

III

t : The time after the change to Braking Range AA.

Headway
distance

Maximum
deceleration
Time (S)Stop of thevehicle

I

II

III

Headway
distance

Rate of change
in deceleration

Maximum
deceleration

Factor of clustering in Readymade Driver Model

Fig. 4 Braking support system with braking factors<Case where Braking Range BB is Generated>
α : The gradient of deceleration after the change from
Braking Range BB to the following range;β´ : The deceleration at the time of change to the following range;
V˝0 : The velocity at the time of change to the following
range;t˝ : The time after the change to the following range.
<Case where Braking Range BB is not Generated>
α : The gradient of deceleration after the change fromthe maximum deceleration to the following range;β´ : The maximum deceleration;V˝0 : The velocity at the time of change to the following
range;t˝ : The time after the change to the following range.

The expressions of velocities in Braking Range AA,
Braking Range BB and the following range are shown
below:

3. DRIVER MODEL OF TBSS

Braking Range AAt1
V = ∫ αtdt = αt 2 + V0
0
2

3.1
(3)

α : The gradient of deceleration from the start of deceleration to the maximum deceleration;
V0 : The velocity at the time of change to Braking Range
AA;

Readymade Driver Model by clustering
The Readymade Driver Model classifies generalcharacteristics of drivers into clusters, and reproduces thecharacteristics. The control elements subjected to clustering are a headway distance that decides the timing ofbraking, the maximum deceleration in the braking range,
relative velocity in the following range, headway distanceat the time when a vehicle stops. However, setting all
IATSS RESEARCH Vol.28 No.2, 2004 •71

TRANSPORTATION

3.2

Tailormade Driver Model built on NN
The Tailormade Driver Model simulates characteristics of individual drivers. This research constructs themodel by using the learning function of NN and having
it learn characteristics of drivers. Fig. 6 shows the input
and output of the Tailormade Driver Model. The input
includes headway distance, relative velocity and relative
deceleration, and the output is the deceleration of thesimulator driver's vehicle. NN uses the deceleration
caused by the driver's braking action as a teacher signal
and repeatedly learns until the error from the value inferred becomes small.
The driver model built on NN was evaluated bybraking simulation. This evaluation is aimed at determining that the model does not brake in a manner significantly different from braking by drivers. For the braking
simulation, only the initial values of headway distanceand deceleration of the leading vehicle are given, and thelater braking is judged by NN. This is because braking
by drivers cannot always be controlled by the same physical quantity, though the same trends are seen even under
the same conditions. Therefore, NN grasps characteristics of a driver based on some data and reflect them inthe model, rather than faithfully reproducing the manipulation once performed by the driver. Fig. 7 shows the72 • IATSS RESEARCH Vol.28 No.2, 2004

Standardized squared
Euclidean distance

25
20
15
10

Group b56.57 mGroup a47.99 m

5
0
h

f

e

a

g

i

j

b

d

c

k

Standardized squared
Euclidean distanceSubjects(a) Headway distance

2.5
2
1.5

Group a2.09 m/s3

Group b2.56 m/s3

Group c2.25 m/s3

1
0.5
0
k

b

d

h

f

j

c

a

i

e

g

Subjects(b) Rate of change in deceleration

2Standardized squared
Euclidean distancecontrol elements in the braking support system for evaluation imposes excessive loads on subjects. For this reason, the rating covered the scope from the start of braking
by subjects to the following range that recognizes safety.
Therefore, the relative velocity to show the timing ofchange to the following range and the headway distanceat the time of stopping were set at the values that fit inwith individual subjects. Readymade Driver Model under this research covers the timing of braking and braking in the braking range, and the control elements
subjected to clustering are defined as the headway distance at the time of start of braking, rate of change in deceleration, and maximum deceleration. Fig. 5 shows therelations between the subjects and the standardized
squared Euclidean distance after a cluster analysis with
the Ward method. In Fig. 5, (a) represents a headway
distance, (b) represents rate of change in deceleration, and(c) represents a maximum deceleration. The data use theresults of the braking experiment described in Section 2.2.
The number of clusters was decided by the subtractive
clustering method. Fig. 5 shows the number of clusters
and the representative values of each cluster. With these
representative values, 18 control elements are set by the
Readymade Driver Model in this research.

Group a1.55 m/s2
Group b3.17 m/s2

1.5Group c2.21 m/s2

1
0.5
0
h

c

e

g

a

b

k

f

d

i

j

Subjects(c) Maximum deceleration

Fig. 5 Result of the cluster analysis

Headway distance
Relative velocity
Relative deceleration

Tailormade
DriverModel

Deceleration

Tailormade Braking Support System (TBSS)

Fig. 6 Tailormade driver model of TBSS

changes in the headway distance between NN and thedriver at Subject a and Subject g. NN braked in a manner similar to the driver, and it was found that the change
in the headway distance was almost the same. As a re-BASIC STUDY ON TAILORMADE BRAKING SUPPORT SYSTEM

Headway distance (m)60Five attempts ofbraking experiment
NN
Driver

4
1

40

2

20

–2

–1

I don't feel
I feelsafetyslightly unsafe

3

0

1

2

Can’t sayeitherI feelslightly safe

I feelsafe(a) Timing of braking

Subject a5

Tailormade
Driver Model

T. HIROSE, T. SAWADA, Y. OGUCHI

–2

–1

0

1

2

Can’t sayeitherI feel slightly
at easeI feelat ease0
Headway distance (m)60

I don't feel
I feelat ease slightly uneasy

1
5

3
4

40
Tailormade
Driver Model
202(b) Magnitude of braking
Subject g

Headway distance 60 (m)Deceleration of the leading vehicle 2 (m/s2)

Fig. 8 Subjective rating scale about the braking
support system

0
0

2

4

6

8
10
Time(s)

12

14

16

Fig. 7 Change in the headway distance between NN
and the driversult of five attempts of braking experiments, it was foundthat the driver model built on NN could learn the characteristics of braking by a driver and model the driver.
The changes in the headway distance at Subject a andSubject g are shown here. The same tendency was foundas with other subjects. It is effective for TBSS to use thisbraking model as the main model.

4. EVALUATION OF TBSS
4.1

Experimental method and evaluation technique
The effect of the system was verified by using TBSS.
The experiment was done by installing TBSS in a vehicle
that was simulated by the driving simulator. TBSS followed
a leading vehicle, and then supported braking as the simulator vehicle approaches the leading vehicle. In so doing,
the driver conducted subjective ratings regarding the senses
of security and safety about the “timing of braking” and“magnitude of braking and degree of approach” of the braking support. Fig. 8 shows the scale of the subjective ratingof TBSS. For information, the system equipped with theReadymade Driver Model controls braking with Expressions (3), (4) and (5) mentioned above, and conducts evaluation by the same experimental method.
4.2

Results of the evaluation
In objective rating, comparisons were made in termsof headway distance and maximum deceleration at the timeof start of braking. Fig. 9 show the differences between
the average values of five attempts of the braking experiment and the set values of Average, Readymade and TBSS.
Average represents the case where the averages of headway distance at the time of start of braking, rate of change
in deceleration and maximum deceleration of all subjects
are set in the system. The y-axis of Fig. 9(a) shows a larger
effect of safety if the set value is larger than the averagevalue of data on headway distance at the time of start ofbraking. If the set value is smaller than the average valueof data on the maximum deceleration in Fig. 9(b), the effect of sense of security is large. Therefore, TBSS shows
greater safety than Average and Readymade in the headway distance at the time of start of braking, and the same
trend applies to the maximum deceleration.
Fig. 10 shows the relationship between subjects andsubjective ratings regarding the headway distances at thetime of start of braking, rates of change in deceleration andmaximum decelerations of all subjects. In Fig. 10, (a) represents the rating of Average, (b) represents the rating ofReadymade Driver Model, and (c) represents the rating of
TBSS. When these systems are compared, the subjective
rating of the Readymade Driver Model is better than thatof the system that sets Average. However, as the ratingsof Subject c and Subject i are negative, it was considerednecessary to study further optimization of the system. For
this purpose, it is considered effective to study the methodology to decide the number of clusters based on the quantity of sense of a human being, rather than objectively
deciding it based on the distribution of data. According to
the results of the subjective rating of TBSS, the subjects givea favorable evaluation to this system. Compared with theevaluations of the Readymade Driver Model, the subjective ratings of TBSS realized significant system optimization for drivers.
IATSS RESEARCH Vol.28 No.2, 2004 •73

TRANSPORTATION

Evaluation
improve

Objective rating
DangerSafety15

Average

Readymade

TBSS

10
5
0

b

c

d

–5
–10

g
e

i

f

h

a

b
a

c

d

e

f

g

Subjects

h

i

a

b

c

d

e

f

g

Subjects

hi

Subjects–15(a) Difference of the headway distance between drivers and systems

Evaluation
improve

Objective rating
UneasyEasy

2

Average

Readymade

TBSS

1
0
–1

a

b

c

d

f
e

g

i
h
Subjects

a

b

c

d

e

f

g

h

i

a

b

c

Subjects

d

e

f

g

h

i

Subjects–2(b) Difference of the maximum deceleration between drivers and systems

Fig. 9 Objective rating about the headway distances at the time of starting braking and the maximum deceleration

According to the objective and subjective ratings,
it is assumed that the subjective ratings of TBSS are
higher because its safety of the timing of braking is higher
than that of Average and Readymade. As for the magnitude of braking, TBSS did not brake with unnecessarily
vigorous deceleration because of the appropriate time ofbraking. It is assumed that the sense of security is improved by this point. Under this research, no results
showed negative evaluations of TBSS.
Example of system operation
Fig. 11 shows a conceptual diagram that assumesa system to install a Readymade system and TDSS in adriver's license. Drivers’ control characteristics learned
by the Readymade system and an individual driver's control characteristics learned by TDSS are incorporated into
the IC chip of the driver's license. This system can use
the functions of TDSS, even if the driver changes vehicle.

uncomfortable in consideration of improvement of safety
and reduction in driving loads. If a driver uses an uncomfortable driving support system, the driver tends to standready to be supported by the system. In this case, even if
the system brings driver safety, it does not give a sense of
security. TDSS is aimed at satisfying individual drivers.
This research studied the construction of braking
models of TBSS in TDSS and the effect of TBSS on drivers in terms of objective and subjective ratings. As a result, the following conclusions were drawn:

4.3

5. CONCLUSION
As for the effect of driving support systems, it isimportant to support drivers without making them feel

74 • IATSS RESEARCH Vol.28 No.2, 2004

(1) The proportion of each braking factor for a drivervaries in the period from the start of braking to stopping. In other words, “changes in braking factors”
were found, i.e., drivers place importance first on therelative deceleration and second on the relative velocity in the first part of braking, and on the headway distance in the latter part of braking;(2) The change in braking factors can be logically incorporated into the control elements of braking support
system.
(3) Readymade Driver Model by clustering is an effective model to classify the attributes of drivers basedon control characteristics and to be incorporated in
the base system of TBSS;BASIC STUDY ON TAILORMADE BRAKING SUPPORT SYSTEM

T. HIROSE, T. SAWADA, Y. OGUCHITiming of braking
Magnitude of braking

DRIVER’S LICENSE
NAME

Subjective rating
poor
good

2

ADDRESS
D.O.B

1
0

b

d

f

g

i
Subjects

–1

c

a
–2

IC chiphe(a) Average

Subjective rating
poor
good

2

42 m

45 m

1
0

a

b

d

e

f

g

h

Subjects

–1

c
i

–2

(b) Readymade

ReadymadeTDSS(TBSS, TASS, TSSS)

Subjective rating
poor
good

2

Brush up
1
0

a

b

c

d

e

f

g

Fig. 11 Working example of the system with thedriver’s licensei

Subjects–1–2h(c) TBSS

Evaluation
improve

Fig. 10 Subjective rating about the timing of braking
and the magnitude of braking(4) As for TBSS by NN, both subjective and objective
ratings on timing and magnitude of braking are favorable;(5) TBSS is adaptable to the control characteristics of individual drivers and improves safety and the senseof security.

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