Computational Intelligence - May 2017 - 66

Desired
MFOC

Preceding
MFOC

LQR
PID

LQR
PID

100

150

Velocity (km/h)

Clearance (m)

90
100

50

80
70
60

0

0

50
Time (s)
(a)

100

50

0

50
Time (s)
(b)

100

2
iTTC (s−1)

Acceleration (m/s2)

Figure 15 Trajectory during cutting in&out scenario: (a) Clearance; (b) Velocity.

0
−2
0

50
Time (s)
(a)

100

0.2
0
−0.2
−0.4

0

50
Time (s)
(b)

100

50
Time (s)
(d)

100

Brake (par)

Throttle (%)

100

50

0

0

50
Time (s)
(c)

100

MFOC

20
10
0
0

LQR

PID

Figure 16 Trajectory during cutting in&out scenario: (a) Acceleration; (b) iTTC; (c) Throttle
opening; (d) Braking pressure.

process, the final policy can steer the car
in the safety distance all the time.
To give a clear evaluation to a policy
for driving safety and comfort, the value
function Eq. (9) is calculated in a back-
ward way.
t

J = 1 / r (s k, a).
2 k =i

(22)

The cumulative performance index,
which considers both the driving safety

66

and comfort, from i to t is shown in
Figure 11 (c). The average values of the
performance index for the initial policy,
the first policy and the final policy during
the learning process are 1072.6, 153.21,
57.16 respectively, which means the final
policy yields the best performance.
Note that the amplitude of the improve-
ment decreases following the policy con-
verging during the later stage of the
learning process.

IEEE ComputatIonal IntEllIgEnCE magazInE | may 2017

B. Testing on Hardware-in-the-loop
Simulator

Hardware-in-the-loop simulator devel-
oped previously is selected as the testing
platform. We run the automotive simu-
lation model in the processor board and
build up different virtual traffic scenarios
to test the intelligent cruise controllers.
The program of the intelligent cruise
control algorithm is downloaded into
the embedded microprocessor. The
microprocessor connects to the simula-
tor by CAN2.0B, which receives the
simulation state from the simulator and
sends the corresponding command to
steer the model.
The control period is set as 0.01 s
during the testing. The proposed ap -
proach MFOC is used to develop the
intelligent cruise controller. The LQR
controller developed in [4] and seg-
ment-PID controller are brought in for
comparisons. Typical virtual traffic sce-
narios [24], such as car following, cutting
in&out and emergency braking, are built
up to test the controllers.
1. Car Following
When a preceding car is detected, the
intelligent cruise control system has to
adjust its velocity to keep a desired dis-
tance to the preceding car. Such a sce-
nario always appears on a highway road.
The preceding car runs at a velocity of
72 km/h, speeds up to 90 km/h with
an acceleration of 1 m/s 2 at 40 s, then it
speeds down to 54 km/h with an accel-
eration of -2 m/s 2 at 65 s. The velocity
curve is shown as the dashed line in
Figure 12 (b).
The state trajectories of the car
steered by the controllers are shown in
Figure 12 and Figure 13. All the con-
trollers can track the distance and the
velocity smoothly. The PID controller
tends to take larger throttle opening and
braking pressure than others. In addition,
fluctuations exist in the acceleration tra-
jectory and the throttle trajectory of the
PID controller, as the controller switches
in different segment. Correspondingly,
the MFOC controller and the LQR
controller take actions more smoothly.
Figure 14 (a) and (b) are the curves
of the comfort index and the safety



Table of Contents for the Digital Edition of Computational Intelligence - May 2017

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