480
ulation phenomenon. Another method to reduce fault
is using alerting equipment. The equipment send out
warning to cause human’s attention when mistake
occurs. For example, ARPA can send out sound and
light warning when the DCPA and TCPA are small-
er than a setting value. Britain, Germany and Japan
develop BNWAS used to monitor steering and sail-
ing on duty. This system used to monitor the alert of
navigator, if the equipment detects that navigator
cannot perform the duty of his, it will send out gra-
dated outspread warning. At first, it will be in cage,
if there is no response it will extend to caption and
other sailor’s room.
3 INTELLIGENT EVALUATION SYSTEM
Traditional collision avoidance is that the sailor
adopts empirical collision avoidance according to
self-experience. It depends on navigator’s individual
intuition to make decision, if the risk is large, it will
be easy to make mistake. Collision avoidance expert
system and decision-making support system spring
up rapidly of late years. They have great auxiliary
effect to vessel collision avoidance.
Human is the principal part in the evaluation sys-
tem of ship maneuvering. We make use of computer
and develop intelligent evaluation system of ship
maneuvering. The system can gather dynamic in-
formation of vessel by AIS, ARPA, infrared and
photo electricity equipments(Thomas et al. 2008).
The information will be sent to the intelligent evalu-
ation system finally, the system will enter into dif-
ferent model according to encounter situation and
environment condition. The result of evaluation is
the current situation of ship.
3.1 The Structure of Intelligent Evaluation System
The evaluation system consist of many models, in-
cluding target ship identification, speculation and
prediction of encounter status, real evaluation of op-
eration, auto-collision avoidance strategy and risk
warning model etc. We can see from Fig. 1, the
evaluation system and operation of navigator form a
closed-loop control system. The system will evaluate
the performance of operation, and send out corre-
sponding signals. In this way it can make up the dis-
advantage of none precision calculation of human,
cut down the probability of human fault occurrence,
and secondly make use of human’s high adaptability
sufficiently.
3.2 Collision Risk Calculation
Ship collision risk calculation is one of the most im-
portant parts in the system. The quantification of
collision risk experience several stages basical-
ly(WU Zhao-lin & ZHENG Zhong-yi 2001). The
first one is traffic flow theory which use ship colli-
sion rate, encounter rate, collision probability to
evaluate the collision risk for special water area. The
second is ship domain and arena which is based on
human praxiology and psychology. (Fuji & Tanaka
1971), (Goodwin 1975) etc. who use this to calculate
collision risk. In the third stage, people have consid-
ered the dCPA(Distance at Closest Point of Ap-
proach) and tCPA(Time at Closest Point of Ap-
proach) in calculation, like (Davis et al. 1980). In the
fourth stage, combine dCPA and tCPA, adopt
weighting method to calculate collision risk at the
beginning(Kearon 1979, Imazu& koyama 1984).
This method exist obvious disadvantage that dCPA
and tCPA are two different variable. Then people
adopt fuzzy theory to combine dCPA and tCPA. At
present mostly research are based on the artificial in-
telligent technology as fuzzy theory, expert system,
neural network to calculate the collision risk(LI Li-
na 2006).
This paper adopt fuzzy compressive evaluation to
calculate CR(collision risk). The comprehensive
evaluation result can be used as subjective evalua-
tion, and also can be as objective one. Furthermore,
system security is a progressively process. We can
get perfect result through assessing the subordina-
tion of the factors. So we don’t use the weighting of
dCPA and tCPA to calculate collision risk, they ap-
plied fuzzy comprehensive evaluation in it. There
are many factors effecting CR. We only consider the
major factors here, the distance between target ship
and local ship d, the position of target ship θ, dCPA,
tCPA. So the target factors’ discourse domain is:
The allocation of target factors weight is:
,
,
,
, and
Expert recommend:
,
,
,
Target evaluation matrix is:
=
tCPA
dCPA
d
r
r
r
r
B
θ
(1)
;10;10;10;10 ≤≤≤≤≤≤≤≤
tCPAdCPAd
rrrr
θ
are target risk membership.