State Estimation for Integrated Longitudinal and Lateral Car

Following Control

Ahmed Chaibet, Moussa Boukhnifer and Cherif Larouci

Abstract— This paper discusses an integrated vehicle con-trol and vehicles states estimations. The non linear observer,based on sliding mode approach is presented for estimationof the vehicle states. The considered technique is appliedto the estimation problem for automated vehicles operatingas two vehicles following. The integrated control consists ofsecond order sliding mode, based on twisting algorithm. Thedeveloped controller is tested in scenario of automated driving.Both performance of observer and controller are presented todemonstrate the effectiveness of the sliding mode observer andsliding mode controller in various maneuvers.

Index Terms— Non linear observer, Sliding mode, Twistingalgorithm, Stop and Go, Vehicle dynamics.

NOMENCLATURE

vx/vy Longitudinal/lateral vehicle speeds

Tc Equivalent drive and brake torque

Trr Rolling resistance torque

Cx/Cy Longitudinal/lateral aerodynamic drag coefficients

m Vehicle mass

Ie f f /Iz

Effective longitudinal inertia/ inertia moment about

the yaw axis through the vehicle center of gravity

Fy f /Fyr Cornering forces at the front tires/rear tires

l f /lr

Distances of the front and rear tires

from vehicle’s center of gravity

α f /αr Slip angle of the front and rear tires

c f /cr Cornering stiffness of the front and rear tires

δ f Steering angle

I. INTRODUCTION

During the two past decades research in a problem of car-

following has attracted the attention of several governmen-

tal institutions with the association of research laboratories

such as in the USA, in Japan and in Europe are focused

respectively on PATH program, the Intelligent Transporta-

tion System program and Chauffeur Project where safety,

traffic congestion’s and the energy consumption are the

mainly topics.These works treat aspects controls lateral and

longitudinal as well in simulation, in experimental phases.

Even though, in the literature, several vehicle models have

been proposed, which take into account variations of critical

parameters such as speed, tire adhesion and which describe

complex phenomena concerning a vehicle behavior. In the

M. Boukhnifer, A. Chaibet and C. Larouci are with LaboratoireCommande et Systemes, ESTACA, 34, rue de Victor Hugo, Levallois-Perret, 92300, France, e-mail:((achaibet, mboukhnifer,

clarouci )@estaca.fr).

control design, the longitudinal mode and the lateral one are

usually treated in a separate way via suitable assumptions.

A design of an integral lateral and longitudinal by using

the real-time trajectory curvature information of the leader

vehicle for vehicle following. Both longitudinal and lateral

controller were designed to guarantee that the vehicle

trailed the leader by tacking on count of the trajectory of

the leader vehicle. The longitudinal controller maintained

a safety distance between the two vehicles and the lateral

controller minimized the alignment angles between the two

vehicles is proposed in [4]. A longitudinal autonomous and

cooperative controller is treated and implemented in [12].

A robust controller synthesis using a backstepping concept

design procedure for both longitudinal and lateral dynamic

of car passenger where performances and stability of the

suggested controller are finally highlighted by some studies

in simulations in various situations is proposed [2]. On the

automated vehicles concept, the vehicle must be able to

achieve a whole of autonomous functions. Among these

functions such as: the heading variation, change maneuver

and a vehicle follower control system for a single vehicle

by maintaining a safety distance [3]. To conclude these

functions, the vehicle is equipped with proprioceptive and

exteroceptive sensors. To measure the whole vehicle states

dynamic is inconceivable for different reasons as economic

and of technical feasibility. In order to overcame these

drawbacks, a solution is to act for the observer concept.

For example, [5] has proposed an approach which combines

an extended Kalman filter for the estimation of the vehicle

dynamics and a Luenberger observer for the estimation of

the road slope. In [6] the author has developed an observer

of the lateral velocity and an integrated control combining

steering and braking/traction in order to improve vehicle

dynamics performance and stability. In this paper a sliding

mode observer based on the algorithm of optimal control

for the second order non linear system is addressed. The

estimation of the vehicle states retains a complexity of the

model and strong non linearities. This observer estimates

the non accessible variables an despite external disturbances

like the variation of road adhesion ( dry, softened, snow

covered, icy...). Sliding mode observer is known to be

robust technique that is an appropriate for the estimating

uncertain systems. High robustness is maintained against of

the various of uncertainties such as external disturbances

and measurement error. This nonlinear observer will be

used to estimate the leader and follower vehicle state’s.

Our emphasis within the development of this observer is

18th IEEE International Conference on Control ApplicationsPart of 2009 IEEE Multi-conference on Systems and ControlSaint Petersburg, Russia, July 8-10, 2009

978-1-4244-4602-5/09/$25.00 ©2009 IEEE 1140

to build the non measurable states and it will be used on

the control law synthesis. The problem considered, in this

paper is the design of an integrated lateral and longitudinal

controller that enables the follower to track the leader

vehicles trajectory by generating suitable steering angle δ f ,

while maintaining a desired following distance by through

of composite torque Tc, is carried out according to the sign

by a engine torque or a brake torque by using the states

estimation.The present paper is organized as follows: in

section 2 a vehicle modeling and positioning are addressed.

In the section 3, the description and the synthesis of the

observer design is given. Section 4 deals with the synthesis

of longitudinal and lateral controllers based on the second

order sliding mode. While the validation of this technique

by some simulation examples of scenarios of a driving is

devoted on the section 5. Conclusion and perspective of

this work are presented in the section 6.

II. VEHICLE MODELING AND POSITIONING

Fig. 1. Relative positioning of vehicles

One gives in the section a simplified three degree-of-

freedom vehicle model has been used for designing the

control strategy in this study. Under the assumptions that

there is no longitudinal slip between the tire and the road,

the pitch, the roll and the vertical dynamics are neglected.

The three degree-of-freedom are the longitudinal, lateral

and yaw motions [4],[2].

vxs = Tc−TrrIe f f

−Cxv2

xsm

+vys ψs

vys =Fy f

+Fyr−Cyv2ys

m−vxs ψs

ψs = 1Iz

[

l f Fy f− lrFyr

]

The characteristic of the tires is assumed to be linear

model where the lateral forces are proportional to the side

slip angle of the tire. The expression of these forces are

given as follows:{

Fy f= 2c f α f

Fyr = 2crαr

where{

α f = δ f−vys +l f ψs

vxs

αr = −vys−lrψs

vxs

The model can be rewritten in canonical form:

vxs = f0 + g0Tc (1)

vys = f1 + g1δ f (2)

ψs = f2 + g2δ f (3)

f0=−TrrIe f f

−Cxv2

xs

m+vys ψs

g0 = 1Ie f f

f1=−2 c f

m

vys + l f ψs

vxs− 2 cr

m

vys − lrψs

vxs−

Cyv2ys

m−vxs ψs

g1 =2c f

m

f2=−l f

Iz2c f

vys + l f ψs

vxs+ lr

Iz2cr

(

vys − lrψs

vxs

)

g2 = lrIz

2c f

(4)

In order to improve vehicle comportment, a kinematics

equations are formulated to express the longitudinal (inter

distance) and lateral displacement [3]. For more clearness,

we give some notations which will be useful thereafter. The

indices (l,s,r) will respectively represent the leader vehicle,

the follower vehicle and the relative variables.

dxr = −vxl+ dyrψl + vxs cosψr +

(

vys + ψsl f

)

sinψr

dyr = −vyl+(lr −dxr) ψl − vxs sinψr+

(vys + ψsl f )cosψr

(5)

The derivative of (5) give us:

{

dxr = a0 + b0Tc + c0δ f

dyr = a1 + b1Tc + c1δ f(6)

where

a0 =− vxl+ dyrψl + dyrψl + f0 cosψr − vxs sinψrψr

+(vys + l f ψs)cosψrψr +( f1 + f2l f )sin ψr

b0 =g0 cosψr

c0 =(g1 + g2l f )sin ψr

a1 =− dxrψl + dxrψl − (vyl− ψllr)− f0 sinψr

− (vys + l f ψs)sin ψrψr +( f1 + f2l f − vxsψr)cosψr

b1 =−g0 sinψr

c1 =(g1 + g2l f )cosψr

(7)

ψr = ψl −ψs (8)

III. SYNTHESIS AND OBSERVER DESIGN

In this section, we present the design of a sliding

mode observer scheme, based on algorithm of sub-optimal

control. This algorithm allows the finite time stabilization

of uncertain second order non linear with incomplete

state measurements [8],[10]. The main characteristic of

this observer is the robustness face to the strong non

linearities of the system and the parameters uncertainties

1141

of modeling. Our aim is to estimate the different unknown

vehicles states in order to make easier the automated

driving. First of all, let give in the following table the

different measurable and non measurable variables, where

the last column of table designate if the state is measurable

(m) or not measurable (nm).

vxl/vxs Longitudinal speed of vehicle leader/ follower m/m

ψl/ψs Yaw angle of vehicle leader/follower nm/m

ψl/ψs Yaw rate of vehicle leader/follower nm/m

dxr/dyr Longitudinal distance/ lateral displacement m/m

dxr/dyrVariation of longitudinal distance/

Variation of lateral displacementnm/nm

ψr Variation of relative heading nm

Table 1: Measurable and not measurable states

A. Estimation of variation relative yaw angle

It is assumed that the relative heading ψr for the two

vehicles is measured by a seteroscopic vision system. The

aim is to estimate the unknown state ψr from ψr. The

relative yaw dynamic can be expressed as follow:

{

x1 = x2

x2 = f2 + g2δ f(9)

with x1 = ψr and x2 = ψr. x2 is not measurable state, f2

and g2, are smooth functions are assumed uncertain. The

first step, we consider the following observer equations:

{ .ψr = vlacet r.vlacet r = ηr(t)

where ηr(t): R+ →R is an auxiliary input to be appropri-

ately selected to guarantee observer convergence in finite

time..ψr = vlacet r is the estimate of relative yaw angle varia-

tion. Now, the second step consists to define the observation

errors:

eψr = ψr − ψr (10a)

evlacet r= vlacet r − vlacet r (10b)

From the (10a) and (10b), we deduct the error dynamic

equations:{

eψr = evlacet r

evlacet r= ψr −ηr(t)

(11)

∣

∣ψl − f2 −g2δ f

∣

∣ ≤ M (12)

where M is a positive constant.

−ψsmax ≤ ψs = f2 + g2δ f ≤ ψsmax (13)

Now, the problem is to find ηr such that the errors (10a)

and (10b) are steered to zero in finite time.

Observer algorithm for the variation of the relative

yaw angle [10], [8]

i) Set γ∗ ∈ (0,1]

ii) Set eψr max = eψr(0)

Repeat, for t > 0, the following steps

iii) I f[

eψr(t)−12eψr max

][

eψr max − eψr(t)]

> 0 then

γ = γ∗ else set γ = 1

iv) I f eψr(t) is an extremal value then set eψr max = eψr(t)v) Apply the control law ηr(t) :

ηr(t) = −γUMaxsign(eψr(t)−1

2eψr max)

Until the end of the control time interval.

�

with UMax > max( Mγ∗ ; 4M

3−γ∗ )

The value of M is obtained empirically by using the

bounded states of vehicle characteristics

B. Estimation of yaw rate of vehicle leader

The yaw rate of the vehicle leader noticed ψl is not

measurable. As we know that the yaw rate of the vehicle

follower is measured by gyroscope. Therefor, we use the

estimate of the variation of the relative yaw angle, previ-

ously developed in order to deduce the variation of the yaw

angle for the leader.

.ψr =

.ψ l − ψs (14)

.ψ l =

.ψr + ψs (15)

C. Estimation of variation of the longitudinal distance

It is assumed that the longitudinal distance is obtained

by the LiDAR. In order to estimate the variation of the

longitudinal distance dxr, we should express the dynamic

of this variable.

Consider the following system{

x1 = x2

x2 = a0 + b0Tc + c0δ f(16)

where x1 = dxr et x2 = dxr.

The second equation of (18) is the same with equation (6),

where Tc et δ f are the control inputs and a0, b0, c0 smooth

functions.

The observer equations can writeen as follows:{ .

dxr = vxr.vxr = ηvxr (t)

.

dxr corresponds to the estimate of the variation longitu-

dinal distance, ηvxr (t) is auxiliary input which ensure the

convergence on finite time of the observer.

The observer errors are givern by:

edxr= dxr − dxr

evxr= dxr −

.

dxr

The equations of the error dynamic can be written:

{

edxr= evxr

evxr= a0 + b0Tc(t)+ c0δ f (t)−ηvxr

(t)

1142

The aim is to find out the positive constant M1

which satisfied this constraint:

∣

∣a0 + b0Tc + c0δ f

∣

∣ ≤ M1 (17)

In order to steer in finite time t the dxr to dxr. The bounded

of these functions a0,b0,c0 ensure the existence of M1.

The following table summarizes the various intervene in

the convergence of the algorithm.

γ∗ ∈ (0,1] γ∗ = 0.75

xMax = x1(0) edsr max = edxr

(0) = 10m

x1(t) edxr(t)

UMax UMax > max(M1γ∗ ;

4M13−γ∗ ) = 15

Table 2: Variables for estimation of the longitudinal

distance

D. Estimation of variation of the lateral displacement

It is assumed that the lateral displacement is given by the

video sensor. In this case we seek the lateral displacement.

The various steps can be summarized as:

Consider the following system{

x1 = x2

x2 = a1 + b1Tc + c1δ f(18)

with x1 = dyr and x2 = dyr.

The observer equation system can be written as:

{ .

dyr = vyr.vyr = ηvyr (t)

where.

dyr is the estimate of the variation of lateral displacement

and sηvyr(t) is the auxiliary input to be determined.

We consider the observer errors as:

edyr= dxr − dyr

evyr= dyr −

.

dyr

The dynamic error equations:

{

edyr= evyr

evyr= a1 + b1Tc + c1δ f −ηvyr

(t)

The input ηvyr(t) and the gain M2 are chosen such as:

∣

∣a0 + b0Tc + c0δ f

∣

∣ ≤ M2 (19)

The different numerical values of the algorithm is ad-

dressed in the following table:

γ∗ ∈ (0,1] γ∗ = 0.75

xMax = x1(0) edyr max = edyr

(0) = 0.05

x1(t) edyr(t)

UMax UMax > max(M2γ∗ ;

4M23−γ∗ ) = 5

Table 3: Variables for estimation of the lateral displacement

IV. SECOND ORDER SLIDING MODE CONTROL

In order to maintain safety distance, the lateral dis-

placement and relative yaw angle of the two vehicle are

controlled to trace their desired values by using second

order sliding mode. The aim is that the follower vehicle

should follow the motion of the leader vehicle.

A. Design methodology

Fig. 2. Inter distance model

In order to control the inter distance, we adopt a constant

headway time policy. The sliding surface is a function of

the inter distance model (see the figure 2):

Slong = dxr + dxrdes (20)

with dxr is the relative distance between the rear of the

leader vehicle and front of the follower vehicle.

dxrdes : reference distance is function longitudinal velocity

of vehicle follower.

dxrdes = d0 + hvxs , where h is the headway time 2 second1, d0 is stopping distance is equal to 2m. For the lateral

control, a vehicle following policy is chosen. The aim of

the control is to minimize a mixed criteria between follower

vehicle lateral displacement and relative yaw angle. This

criteria is: σlat = dyr + λ ψr.

λ is a weighting coefficient. The lateral surface is defined

with the following manner:

Slat = σlat + c1σlat + c2

∫ t

0σlatdτ (21)

The sliding surfaces are: S =[

Slong,Slat

]T. The equivalent

control inputs are computed as follow:

S =[

Slong, Slat

]T= 0.

S =

[

dxr + hvxs(

dyrλ ψr

)

+ a1

(

dyr + λ ψr

)

+ a2 (dyr + λ ψr)

]

(22)

S = G+ B

[

Tc

δ f

]

(23)

with

G =

[

dxr + h f0

a1 + λ ψl − f2 + c1

(

dyr + λ ψr

)

+ c2 (dyr + λ ψr)

]

and

B =

[

hg0 0

b1 c1 −λ g2

]

Since our aim is to apply the second order sliding mode.

1French law that specifies that drivers are not allowed to drive with aheadway time less than 2 seconds

1143

It offers good properties such as the elimination of the

chattering phenomenon. The Twisting Algorithm [11] is

applied to the derivative input δ f , Tc.

u =

−u |u| >∣

∣ueq

∣

∣

−KMsign(S) i f SS ≤ 0, |u| ≤∣

∣ueq

∣

∣

−kmsign(S) i f SS > 0, |u| ≤∣

∣ueq

∣

∣

(24)

Where KM and km are coefficients, determined to respect

the four conditions of applications of the twisting

Algorithm.

V. SIMULATION RESULTS

In this section, several simulations have been carried in

order to show the effectiveness of the proposed algorithm

for car-following problem.

A. Lance change maneuver

This maneuver is summarized as follows: the two ve-

hicles are located on the same right lane, are spaced of

a distance from 20 m evolve with the same longitudinal

speed of 16.66 m/s. The observer obviously synthesized is

used to estimate the non measurable variables which consist

of: the variation of relative yaw angle, variation of the

lateral displacement, variation of the longitudinal distance

and the yaw rate of the vehicle leader are illustrated on

figures (3,(b)), (3,(c)), (3,(d)), (3,(e)). We notice through

these figures, one simultaneously represented the measured

signal and its estimated. The following remarks can be

deduced:

• The estimated states converge quickly toward the real

states.

• The performances obtained are good as well in dy-

namics as in statics.

• The observation errors are steered to zero in finite time.

At time t = 0sec, the leader initiates a cut in maneuver

to the left lane. The aim of the follower vehicle is to make

a lane change maneuver by maintaining a safety distance

by acting on the steering angle of the front wheels and the

composite torque. However, it has also to perform braking

as the actual spacing is smaller by 15.32m than the desired

spacing of 35.32m which corresponds to a headway time

of 2sec. The profile of lane change maneuver is carried

out successfully with small steady state error (see figure

(4,(a)).The evolution in time of the sliding manifold which

correspond to the spacing error (4d). It can be noticed the

convergence towards zero which proves the reached desired

spacing.

B. Stop and Go maneuver

We want, through this maneuver, to give an outline of

the daily traffic car following in a case of stop and go.

Initially, the follower vehicle is located on the left lane

and the vehicle leader is located on the right lane, while

the inter distance is 27.5m. The vehicle leader moves

with an initial velocity of 36km/h, the following vehicle

evolves with initial velocity of 60km/h. Since the desired

-3.5 -3 -2.5 -2 -1.5 -1 -0.5 0 0.5-1

-0.5

0

0.5

1

1.5

2

Derivative of Slat

Sla

t

-3.5 -3 -2.5 -2 -1.5 -1 -0.5 0 0.5-1

-0.5

0

0.5

1

1.5

2

Derivative of Slat

Sla

t

0 2 4 6 8 10 12 14 16 18 20-0.12

-0.1

-0.08

-0.06

-0.04

-0.02

0

0.02

0.04

0.06

0.08

Time (sec)

Variation o

f re

lative y

aw

angle

(R

ad/s

)

Estimated

Measured

(a) (b)

0 2 4 6 8 10 12 14 16 18 20-0.08

-0.06

-0.04

-0.02

0

0.02

0.04

0.06

0.08

Time (sec)

yaw

rate

of

vehic

le leader

(Rad/s

)

0 2 4 6 8 10 12 14 16 18 20-6

-5

-4

-3

-2

-1

0

1

Time (sec)

Variation o

f lo

ngitudin

al dis

tance (

m/s

)

Estimated

Measured

(c) (d)

0 2 4 6 8 10 12 14 16 18 20-1

-0.5

0

0.5

1

1.5

Time (sec)

Variation o

f la

tera

l dis

pla

cem

ent

(m/s

)

Estimated

Measured

(e)

Fig. 3. Profile of lane change maneuver. (a) : Sliding manifold (Slat ,Slat),(b): Variation of relative yaw angle, (c): Yaw rate of vehicle leader, (d):Variation of longitudinal distance, (e): Variation of lateral displacement

0 50 100 150 200 250 300 350-0.5

0

0.5

1

1.5

2

2.5

3

3.5

Longitudinal position (m)

Latr

al positio

n (

m)

0 2 4 6 8 10 12 14 16 18 20-1.2

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

Time (sec)

Ste

ering a

ngle

(deg)

0 2 4 6 8 10 12 14 16 18 20-1.2

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

Time (sec)

Ste

ering a

ngle

(deg)

(a) (b)

0 2 4 6 8 10 12 14 16 18 20-1.5

-1

-0.5

0

0.5

1

1.5

Time (sec)

Longitudin

al accele

ration (

m/s

2)

0 2 4 6 8 10 12 14 16 18 20-1.5

-1

-0.5

0

0.5

1

1.5

Time (sec)

Longitudin

al accele

ration (

m/s

2)

0 2 4 6 8 10 12 14 16 18 20-35

-30

-25

-20

-15

-10

-5

0

5

Time (sec)

Spacin

g e

rror

(m)

0 2 4 6 8 10 12 14 16 18 20-35

-30

-25

-20

-15

-10

-5

0

5

Time (sec)

Spacin

g e

rror

(m)

(c) (d)

Fig. 4. Profile of lane change maneuver: (a) : lateral and longitudinalpositions of the leader (*,-.) and the follower (o,-), (b): Steering angle,(c): Longitudinal acceleration of vehicle follower, (d): Spacing error

1144

longitudinal distance is of 35.32m. In order to control this

distance, the follower must decreases while decelerating

to reach the speed of 35km/h and at the same time, the

vehicle leader accelerates to reach at the end of 10 seconds

the value of 60km/h and makes a change of heading

direction at the distance of 60 m with a yaw angle ψl= 0.1

rad, then it continues its trajectory with this same speed.

To preserve the desired inter distance, the follower vehicle

accelerates one again to reach the speed of 60km/h. At t=

14sec, the vehicle leader carries out a deceleration until

to stop, and thereafter at t = 40sec the vehicle accelerates

to reach a velocity of 60km/h with this same speed for

one duration of 10sec. The leader decelerates (60km/h to

35km/h) and finish the trajectory by an uniform movement

of 36km/h. The velocities profile are given on figure (5,a).

We can note that this maneuver is carried out with

success with respect to the passengers comfort where the

reasonable boundaries in acceleration range are from -0.3g

to 0.2g as depicted on figure (5,d). It can be seen that the

desired spacing distance is reached (see figure (5,c)).

According to the figures (5,(e),5,(f)), which correspond

to the variation of the lateral displacement, the variation

of longitudinal distance, we can also note that both plots

are great identical which proves the convergence of the

observer in a finite time with a negligible steady state

error and the estimated curve follows the measured one.

0 10 20 30 40 50 60 70 800

10

20

30

40

50

60

Time (sec)

Longitudin

al speed (

Km

/h)

0 10 20 30 40 50 60 70 80-1500

-1000

-500

0

500

1000

1500

Time (sec)

Com

posite torq

ue (

N.m

)

0 10 20 30 40 50 60 70 80-1500

-1000

-500

0

500

1000

1500

Time (sec)

Com

posite torq

ue (

N.m

)

(a) (b)

0 10 20 30 40 50 60 70 80-10

-8

-6

-4

-2

0

2

Time (sec)

Spacin

g e

rror

(m)

0 10 20 30 40 50 60 70 80-4

-3

-2

-1

0

1

2

3

Time (sec)

Longitudin

al A

ccele

ration (

m/s

ec

2)

0 10 20 30 40 50 60 70 80-4

-3

-2

-1

0

1

2

3

Time (sec)

Longitudin

al A

ccele

ration (

m/s

ec

2)

(c) (d)

0 10 20 30 40 50 60 70 80-2

-1.5

-1

-0.5

0

0.5

Time (sec)

Variation o

f la

tera

l dis

pla

cem

ent(

m/s

)

Estimated

Measured

0 10 20 30 40 50 60 70 80-6

-4

-2

0

2

4

6

8

10

12

Time (sec)

Variation longitudin

al dis

tance(m

/s)

Estimated

Measured

(e) (f)

Fig. 5. Stop and Go maneuver. (a): Leader (-.) and follower (-) longitu-dinal speed, (b): Composite torque, (c): Spacing error, (d): Longitudinalacceleration of follower vehicle, (e): variation of lateral displacement,(f):variation of longitudinal distance

VI. CONCLUSION

A non linear control approach for the vehicle following

problem has been developed. By estimating the non mea-

surable states with sliding mode observer. This observer

is able to build the non measurable states. Afterwards,

these different estimated states were implemented on the

integrated longitudinal and lateral control problem. The

observer and control law are based on a variable structure

theory. Simulations have been carried out to illustrate

the ability of this approach to give well performance of

the states estimation and control law synthesis in some

scenarios of automated driving.

REFERENCES

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