danger prediction by case-based approach on expressways
DESCRIPTION
Danger Prediction by Case-Based Approach on Expressways. C. Y. Fang, P. Y. Wu, S. L. Chang, and S. W. Chen National Taiwan Normal University Department of Computer Science and Information Engineering. Outline. Introduction System Flowchart and Database Weighted Relational Map - PowerPoint PPT PresentationTRANSCRIPT
Danger Prediction by Case-Based Approach on Expressways
C. Y. Fang, P. Y. Wu, S. L. Chang, and S. W. Chen
National Taiwan Normal UniversityDepartment of Computer Science and
Information Engineering
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Outline
Introduction System Flowchart and Database Weighted Relational Map The Matching Algorithm Experimental Results Conclusions and Future Work
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Introduction
Driving risk reduction approach Passive approach
To reduce the degree of injury in case of an accident Examples: seat belts and airbags
Active approach To prevent accidents in advance Example: driver assistance system
The dangerous driving event prediction system To predict dangerous driving events Based on the weighted relational map
the driving factors for the host vehicle the driving factors for nearby vehicles the driving factors for the roadway conditions
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Relational Map for Driving Event Construction
Driving Factors
Relational Map Matching
Degree of Danger Generation
Warning Output
Dangerous?
Relational Map of Dangerous Case Database
System Flowchart
yes
no
yes
Accident Occurred?
Dangerous Case Insertion
no
Map CMap D
Map C
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Dividing Database into Sub-Databases
Dangerous Case Database
InterchangeSection
Sub-Database
• To speed the matching process• Dangerous case database is divided into four
sub-databases based on road conditions.
OrdinarySection
Sub-Database
TollboothSection
Sub-Database
TunnelSection
Sub-Database
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Dividing Sub-Database into Classes
InterchangeSection
Sub-Database
Cloudy Class
Hazy Class
Misty Class
Rainy Class
Snowy Class
Sunny Class
• Each sub-database is divided into six classes based on weather conditions.
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Outline
Introduction System Flowchart and Database Weighted Relational Map The Matching Algorithm Experimental Results Conclusions and Future Work
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Driving Factors
Input data for nearby vehicles Lateral distance Longitudinal distance Relative lateral speed Relative longitudinal speed
The driving factors for nearby vehicles are:(1) the left-front vehicle and the host vehicle are close
(2) the preceding vehicle and the host vehicle are close (3) the right-front vehicle and the host vehicle are close
(4) the left vehicle and the host vehicle are close (5) the right vehicle and the host vehicle are close (6) the left-rear vehicle and the host vehicle are close (7) the following vehicle and the host vehicle are close (8) the right-rear vehicle and the host vehicle are close
Lateral distance
Longitudinal distancen3
n1 n2
Host vehicle
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Driving Factors Input data for host vehicle
Lateral distance to left/right obstacle Turning angle of front wheel Turn signal on/off Speed of host vehicle Driver’s level of alertness
The driving factors for host vehicle are:(9) the host vehicle turns left(10) the host vehicle turns right(11) the host vehicle speeds up(12) the host vehicle slows down(13) driver’s level of alertness increases(14) driver’s level of alertness decreases(15) the host vehicle turns on the turn signal (16) the host vehicle maintains constant speed(17) the host vehicle goes straight
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Driving Factors
Driving factors for roadway:(18) smooth and straight roadway (19) smooth and curved left roadway (20) smooth and curved right roadway (21) downgrade and straight roadway (22) downgrade and curved right roadway (23) downgrade and curved left roadway (24) upgrade and straight roadway (25) upgrade and curved left roadway (26) upgrade and curved right roadway
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A Relational Map Each node represents one driving factor.
Node 18 : smooth and straight roadway Node 16 : the host vehicle maintains constant speed Node 1 : the left-front vehicle and the host vehicle are close Node 11 : the host vehicle speeds up Node 9 : the host vehicle turns left
Two requirements to generate new nodes Fixed sampling interval Any driving factors occurring between samples
18
1
11
116
9
16
Tt1t2t3t4t
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Weighted Relational Map
18
1
11
116
90.50.7
0.9 0.7
0.5
0.9
0.4
0.5
0.8
0.8 0.7
0.9
160.9
0.7
Tt1t2t3t4t
18
1
11
116
9
16
Node value
Node number
Link weight
Relational map
Weighted Relational map
Node number
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The Node Value The node value (the importance of node)
Initialized with a constant Increased or decreased based on the relationships with the
previous, present and following nodes
Examples of increasing node values The left-front vehicle and the host vehicle are close at time t -1,
and the host vehicle turns left at time t. The host vehicle speeds up at time t -1, and slows down at time t.
Examples of decreasing node values The host vehicle turns on the turn signal at time t -1, and turns
left at time t. The host vehicle turns on the turn signal at time t -1, and turns
right at time t.
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The Weight Between Adjacent Nodes
The link weight:
: value of driving factor at : value of driving factor at α : a constant
∆t : the time between successive driving factors
The weight is large if and are very different. Example: vehicle changes its speed
1t1tltl
))min(1(Δ 1
1
t-
t
t
t-
ll,
ll
tW
t
1tltl
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Outline
Introduction System Flowchart and Database Weighted Relational Map The Matching Algorithm Experimental Results Conclusions and Future Work
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Defining the Driving Factor Sets for Each Node Three driving factor sets for :
previous, present and following sets : set for node at time : set for node at time : set for node at time
in
in
in
1tt
1t
1,, tn tiS
tn tiS ,,
1,, tn tiS
in
18
1
11
116
90.50.7
0.9 0.7
0.5
0.9
0.4
0.5
0.8
0.8 0.7
0.9
160.9
0.7
Tt1t2t3t4t
Weighted Relational map
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Relationship Between Adjacent Nodes
J
knnn
nnn
nt yt i
tktitk
tytity
tinnR
1
1,,
),min(
),min(),(
1,,1,
1,,1,
,
Jy ,,1
tlink weigh : valuenode:
1,, tn tiS
tin ,
tin ,
1,1 tn
1, t Jn
Tt 1t 2t
Node value
Link weight
1, tyn1, tyn
1,, tyti nn
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Relationship Between the Nodes on Same Layer
)(min),(,,...,1,, tknJktytx nnR
yxJyx and ,,1,
tn txS ,,
tn ,1
tJn ,
tn ,1
tJn ,
T1t t 1t
tkn ,
Node value
tkn ,
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Table from Weighted Relational Map Set
null null 0.9/16
0.5/18 null 0.318/1,0.182/11
0.9/16 0.4/11 0.9/1
0.4/16 0.4/1 0.4/1
0.444/1,0.356/11 null 0.4/9,0.4/16
0.9/1 0.9/16 end
0.9/1 0.9/9 end
1,,
~tn ti
Stn tiS ,,
~1,,
~tn ti
Stin ,
3t16
4t18
2t1
2t11
t91t1
t16
18
1
11
116
90.50.7
0.9 0.7
0.5
0.9
0.4
0.5
0.8
0.8 0.7
0.9
160.9
0.7
Tt1t2t3t4t
Weighted Relational map
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Matching Algorithm
Map C is the current weighted relational map formed in real time, and is the driving factor in C.
Map D is the dangerous weighted relational map in the database, is the driving factor in D.
: the similarity between two maps N : the number of driving factors in C : the similarity between two driving
factors.
Cn
titDnt
ti
nnN
DCSim1,
212,
),(~max1),(~,,
),(~21 ,, tit nn
in
n
Sim
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Matching Algorithm
)1,1,,(~~21,,1 2211 ttnnII titrt
Cn
titDnt
ti
nnN
DCSim1,
212,
),(~max1),(~,,
) ~
~
~
~
~
~ (
31),(~
1
1
1
1,, 21
t
t
t
t
t
ttit
U
I
U
I
U
Inn
)1,1,,(~~21,,1 2211 ttnnUU titrt
where | | : scalar cardinality
: fuzzy intersection
: fuzzy union
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Matching Algorithm
: fuzzy driving factor set for node at time in the map C.
: fuzzy driving factor set for node at time in the map D.
T( ) is the weighting function.
1,tn
Cttnzx
Dttinyx y
y
z
z
tit
Sn Sn x
x
x
x
Dtn
Ctn
titrtt
n
ntT
nntT
tTStTS
ttnnII
11,1, 12,2,
22,11,
21
~ ~
21
21,11,
21,,11
))1),()1(min(
,)1),()1(min(
min(
)))1(~())1(~((
)1,1,,(~~
2,tin
Ctn t
S 1, 11,
~
Dtn ti
S 1, 22,
~
11t
12t
)(log)(
t
tTe
and are constants.
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Outline
Introduction System Flowchart and Database Weighted Relational Map The Matching Algorithm Experimental Results Conclusions and Future Work
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Experimental Results
Sim(C,D)=1
18 1
14
11 1
9
11
21
0.00.51.01.52.0
T
0.5
0.5
0.5
0.5
0.5
0.9
0.9
0.9
0.957
0.2
0.444
0.833
0.6
0.6
0.833
0.182
0.5
0.5
0.182
18 1
14
11 1
9
11
21
0.5
0.5
0.5
0.5
0.5
0.9
0.9
0.9
0.957
0.2
0.444
0.833
0.6
0.6
0.833
0.182
0.5
0.5
0.182
Map D
Map C
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Example (1)
Map C1
Sim(C1,D) =0.48
18
0.5
18 1
0.5 0.9
0.957Sim(C2,D) =0.65
Map C2
18 1
11
9
0.5 0.9
0.9
0.9
0.957
0.2
0.444Sim(C3,D) =0.66
Map C3
18 1
14
11 1
9
0.5
0.5
0.5
0.9
0.9
0.9
0.957
0.2
0.444
0.833
0.6
0.6
0.833
Sim (C4,D) =0.74
Map C4
18 1
14
11 1
9
11
21
0.00.51.01.52.0
T
0.5
0.5
0.5
0.5
0.5
0.9
0.9
0.9
0.957
0.2
0.444
0.833
0.6
0.6
0.833
0.182
0.5
0.5
0.182
Map D
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Example (1)
Sim(C5,D)=1
18 1
14
11 1
9
11
21
0.5
0.5
0.5
0.5
0.5
0.9
0.9
0.9
0.957
0.2
0.444
0.833
0.6
0.6
0.833
0.182
0.5
0.5
0.182
18 1
14
11 1
9
11
21
0.00.51.01.52.0
T
0.5
0.5
0.5
0.5
0.5
0.9
0.9
0.9
0.957
0.2
0.444
0.833
0.6
0.6
0.833
0.182
0.5
0.5
0.182
Map D
Map C5
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Example (2)
Sim(C,D1)= 0.508
0.00.51.01.5
T
Map C18 1
14
11 1
9
0.5
0.5
0.5
0.9
0.9
0.9
0.957
0.2
0.444
0.833
0.6
0.6
0.833
Map D118 1
11
9
0.5 0.9
0.9
0.9
0.957
0.2
0.444
Map D218 1
14
11 1
9
11
21
0.5
0.5
0.5
0.5
0.5
0.9
0.9
0.9
0.957
0.2
0.444
0.833
0.6
0.6
0.833
0.182
0.5
0.5
0.182
Sim(C,D2)= 0.743
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Example (3)
0.00.51.01.52.0
T
Sim(C,D2)= 0.938
Map D2
Map C
18 1
14
11 1
9
11
21
0.5
0.5
0.5
0.5
0.5
0.9
0.9
0.9
0.348
0.25
0.222
0.842
0.2
0.2
0.842
0.4
0.5
0.5
0.4
18 1
14
11 1
9
11
21
0.5
0.5
0.5
0.5
0.5
0.9
0.9
0.9
0.348
0.25
0.222
0.211
0.2
0.2
0.211
0.118
0.5
0.5
0.118
Map D118 1
14
11 1
9
11
21
0.5
0.5
0.5
0.5
0.5
0.9
0.9
0.9
0.348
0.25
0.222
0.316
0.2
0.2
0.316
0.222
0.5
0.5
0.222
Sim(C,D1)= 0.967
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Example (4)
T
18 1
14
11 1
9
11
21
0.5
0.5
0.5
0.5
0.5
0.9
0.9
0.9
0.957
0.2
0.444
0.833
0.6
0.60.833
0.182
0.5
0.50.182
0.00.51.01.52.0
22 3
14
11 3
20
11
10
0.5
0.5
0.5
0.5
0.5
0.9
0.9
0.9
0.4
0.2
0.444
0.6
0.6
0.60.6
0.125
0.5
0.50.125
Sim(C,D2)= 0.081
Map C
Hit with right-front vehicle
Map D2
Hit with left-front vehicle
18 6
14
12 6
9
12
21
0.5
0.5
0.5
0.5
0.5
0.9
0.9
0.9
0.4
0.2
0.444
0.6
0.6
0.6
0.6
0.125
0.5
0.5
0.125Map D1
Hit with left-rear vehicle
Sim(C,D1)= 0
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Conclusions and Future Work
The proposed system Predicting dangerous driving events based on the
weighted relational map which is constructed by the driving factors
Using fuzzy matching algorithm to get the similarity between two weighted relational maps
Future Work Improving the method to experimental threshold of
level of danger Hoping test vehicles could equip with the prototype
system in the future
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Thank you for your attention!