1. Dangerous Driving Condition Analysis in Driver Assistance Systems
C. Y. Fang, C. F. Chiou, C. L. Chen, and S. W. Chen
National Taiwan Normal University
Department of Computer Science and Information Engineering

2. Outline Introduction System Flowchart
Fuzzy Rough Sets Attribute Reduction
Rule Selection
Experimental Results
Conclusions and Future Work

3. Introduction Driver assistance systems (DAS) Detection systems
To detect dangerous driving scenario
To prevent traffic accidents
Detection systems
Road sign and traffic signal detection systems
Road and lane detection systems
Obstacle detection systems
The driver’s drowsiness detection systems
Neighboring vehicle motion detection systems …

4. Block Diagram of the Detection and Analysis Components of a DAS…
Sensor 1
Sensor 2
Sensor 3
Sensor n
Detection
Component …
Detection system 1
Detection system 2
Detection system 3
Detection system n
Analysis
Component
Dangerous event analysis subsystem
Dangerous Driving Condition Analysis Systems
Too many warnings create confusion for the driver
To avoid sending incorrect warnings by integrating the analysis of information supplied by the various detection systems
To give the alert at the right time and for the right scenario
Warning output

5. System Flowchart
Each kind of the data can be regarded as an attribute.
Then these reduced data can be transferred into dangerous detection rules by a rule selection method.
These rules can be used to construct the fuzzy Petri nets for reasoning.
If the current driving condition is danger, then the system will output the warning message.
We focus today’s presentation on introducing the learning stage.

6. Fuzzy Rough Sets To remove redundant or misleading attributes
Advantages of Rough Sets:
1) Only a few samples
2) No prior knowledge
3) Tolerance to noises or uncertainties
4) Semantics-preserving

7. Attributes Class 1: Static attributes Three kinds of attributes:
Class 2: Dynamic attributes
Class 3: Environmental attributes
Class 1: Static attributes
No.
D/F
Attribute
Data Source
Value Range C1 D
Section of highway
GPS + Database
1 – 4 C2
Road sign
Image processing
1 – 35
Before attribute reduction, we need to list all the attributes as many as possible.
Some of these attributes are useful to analyze the dangerous events, but some are not.
Our system will select the useful attributes in the attribute reduction stage.
Section of highway: ordinary section, interchange section, tunnel section and tollbooth section.
D: discrete attribute
F: fuzzy attribute
ordinary section, interchange section, tunnel section and tollbooth section

8. Attributes Class 2: Dynamic attributes D/F Attribute Data Source
Value Range C3 F
Driver’s level of consciousness
Image processing
0 – 1 C4
Wheel friction
Pressure sensor C5 D
Driver’s vehicle color
Input by user
0 – 9 C6
Position along the highway
GPS
0 – length of highway C7
Bias of lane central
-1.5 – 1.5 C8
Lane number
1 – 4 ; C9
Speed
Techograph
0 – 120
C10
Acceleration
-4 – 4
C11
Headlight
0 / 1
C12
Turn
0 / 1 / 2
C13
Steering wheel
C14
Brakes
Dynamic attributes indicate the attributes which are relative to the driver or the vehicle
and the values of attributes may change with the time when driving.

9. Attributes Class 2: Dynamic attributes D/F Attribute Data Source
Value Range
C15 D
Brake signal
Techograph
0 / 1
C16 F
Speed differential
Image processing
-120 – 120
C17
Lateral distance between neighboring vehicles
-1.5 – 1.5
C18
Longitudinal distance between neighboring vehicles
-1000 – 1000
C19
Neighboring vehicle’s color
0 – 9
C20
Neighboring vehicle’s turn signal
Neighbor’s techograph
0 / 1 / 2
C21
Neighboring vehicle’s brake signal
C22
Accumulation of acceleration
-9 – 9
C23
Accumulation of brake
C24
Accumulation of turn
-4 – 4

10. Attributes Class 3: Environmental attributes
D/F
Attribute
Data Source
Value Range
C25 D
Weather Forecast
Internet information system
1 – 5
C26
Time of day
System time
1 – 6
C27 F
Visibility
Image processing
0 – 1
We only list part of the attributes.
We do not know which attributes are relative to the degree of danger, and which ones are not.
Here we only list part of the attributes.
We can add any other attribute if necessary.
But we do not know which attributes are useful, and which ones are not.

11. Attribute Reduction Remove attributes: 1) Redundant attributes
“position along the highway” vs. “section of highway”
four wheel speed …
2) Misleading attributes
plate number
vehicle colors
So we designed an attribute reduction method to remove the redundant and misleading attributes.
It means that we can select the important or useful attributes by using this method.
For example, “position along the highway” and “section of highway” may be similar attributes that
We can only use one of them to analyze the dangerous driving events.
And plate number may be not affect the safety of the driving.

12. Fuzzy Rough Sets Attribute Reduction (FRAR)
Fuzzy-rough dependence function
To measure how good to use the condition attribute set P to classify the decision attribute set D
R’ is a fuzzy-rough dependence function which can measure
How good to use the condition attribute p to classify the decision attribute D.
If the value of r’ is high, it indicates the attributes in set P can well classify the attributes in set D.
D: the decision attribute set

13. The QuickReduct Algorithm (for FRAR)
Let C be the condition attribute set and D be the decision attribute set, and R indicate the final reduced result
At the initial, R is set to empty.
At each iteration, the system will select one attribute x from set C which can increase the value of fuzzy-rough dependence function r’.
If r’ R union X is larger then the r’ R, then the attribute x is added into set R.
If not, then the system goes to stop.
We know that this algorithm can not find the global optima solution, but it is an efficient algorithm.

14. An Example
Attributes
Conditions
C17
C18
C19 D1 O1 80 1 O2 55
0.45 O3 25
0.95 O4 10 O5 4 O6 6 5
(a) Membership functions of lateral distance.
(b) Membership functions of longitudinal distance.
(c) Membership functions of degree of danger.
shortX longX
shortY longY
This example can explain how the quick-reduct algorithm works.
This table shows three condition attributes C17 C18 C19, one decision attribute D1, and six conditions O1 to O6.
O4 indicates the longitudinal distance between the host vehicle and the preceding vehicle is 10 meters,
This condition may be a dangerous condition, so its degree of danger is equal to one, which is very high.
In O5 condition, the longitudinal distance between the host vehicle and the preceding vehicle is also equal to 10 meters,
But the lateral distance between them indicates they are in different lanes, so its degree of danger is equal to zero.
These values can be transferred into fuzzy numbers using the membership functions shown here.
C17, C18: condition attributes indicating the “lateral distance”, “longitudinal distance” between the host vehicle and the preceding vehicle (in meters)
C19: the “neighboring vehicle’s color”, which is chosen between blue (0) and green (1).

15. Lower approximation of each concept X:
An Example
The universal set U can be partitioned by the attributes:
Lower approximation of each concept X:
For example:

16. An Example
The fuzzy positive regions can be computed by
For example:

17. C17 is the first selected attribute.
An Example
The fuzzy-rough dependence function can be analyzed using
For example: (at the first iteration)
Finally, the values of the fuzzy-rough dependence function after the first iteration are …
C17 is the first selected attribute.
R ={C17 }

18. C18 is the second selected attribute.
An Example
The second iteration:
C18 is the second selected attribute.
R ={C17,C18 }
The third iteration:
At the second iteration, since we have selected the attribute C17.
The system computes r’C17 union C18 and r’C17 union C19, and compares the value of them.
At the third iteration, since the r’ value does not increase, so the attribute C19 is not added into set R.
Finally, the final reduced result R contains only c17 and c18.
C19 is not selected.
R ={C17,C18 }

19. Outline Introduction System Flowchart
Fuzzy Rough Sets Attribute Reduction
Rule Selection
Experimental Results
Conclusions and Future Work

20. Rule Selection
shortX longX
shortY longY
C17
C18 D1 O1
shortX
longY
safe O2 O3
shortY
danger O4 O5
longX O6
C17
C18
C19 D1 O1 80 1 O2 55
0.45 O3 25
0.95 O4 10 O5 4 O6 6 5
After the quickreduct algorithm, the system selects two condition attributes C17 and C18.
So the table can be reduced as the left one.
Transfer these attributes into their corresponding fuzzy numbers, we can obtain a table like the right one.
Each condition now can be written as an association rule.
The rule selection method will select the useful rules and reduce the total number of these rules.
O1: If (C17 is shortX) and (C18 is longY) then (D1 is safe).
O3: If (C17 is shortX) and (C18 is shortY) then (D1 is dangerous). …

21. Rule Measure Selecting rules ( ) according to: 1) Support:
The joint probability of S and Q indicates the frequency of occurrence of such driving conditions.
2) Confidence:
Confidence is the conditional probability which indicates the relationship between S and Q.
The higher the probability is, the stronger their relationship will be.
A rule representing a dangerous driving condition which seldom occurs is also important.
The system selects association rules, S implies Q, according to two criteria.

22. Rule Selection Algorithm
Step 1:
Initially, the rule set Rs and the selected attribute set S are both empty. C is the condition attribute set.
Step 2:
for all association rules Y whose form is
and satisfy
If (Supp(Y) > Ts and Conf(Y) > Tc) or (Conf (Y) > T’c)
then insert rule Y into Rs
Step 3:
If ( ) or (all rules Y have been examined) then stop, else go back to Step 2.
T’c > Tc

23. Rule Selection Each condition is regarded as an association rule.
O1: If C17 is shortX and C18 is longY then D1 is safe.
O2: If C17 is shortX and C18 is longY then D1 is safe.
O3: If C17 is shortX and C18 is shortY then D1 is dangerous.
O4: If C17 is shortX and C18 is shortY then D1 is dangerous.
O5: If C17 is longX and C18 is shortY then D1 is safe.
O6: If C17 is longX and C18 is shortY then D1 is safe.
Applying the rule selection algorithm,
the rule set Rs finally contains the
following three rules.
If C17 is longX then D1 is safe.
If C18 is longY then D1 is safe.
If C17 is shortX and C18 is shortY
then D1 is dangerous.
C17
C18 D1 O1
shortX
longY
safe O2 O3
shortY
danger O4 O5
longX O6

24. Outline Introduction System Flowchart
Fuzzy Rough Sets Attribute Reduction
Rule Selection
Experimental Results
Conclusions and Future Work

25. Experimental Results The degrees of danger (D1):
{very safe, safe, normal, dangerous, very dangerous}
The thresholds of the rule selection algorithm:
Ts = 0.05, Tc = 0.8, and T’c = 0.85
A data generation system is used to simulate the dangerous driving conditions.

26. Experiment 1 : Whilst a preceding vehicle and the host vehicle are driving in the same lane, the host vehicle begins to accelerate.
1st
2nd
3rd C1
+0%
C15 C2
C16
+31.6%
+0.6% C3
C17 C4
C18
+51.8% C5
C19 C6
C20 C7
+12.5%
C21 C8
C22 C9
+21.3%
+0.5%
C23
C10
+10.0%
C24
C11
C25
C12
C26
C13
C27
C14
Speed differential
Longitudinal distance between neighboring vehicles
We develop a simulation system to generate the experimental data.
So the experimental data shown here are not real-world data.

27. Experiment 1
Whilst a preceding vehicle and the host vehicle are driving in the same lane, the host vehicle begins to accelerate.
40 conditions  40 association rules
Selected Rules
Supp(Y)
Conf(Y) Y1
If C18 is very short then D1 is very dangerous.
0.12
0.83 Y2
If C18 is normal then D1 is safe.
0.20
0.89 Y3
If C18 is long then D1 is safe.
0.15
1.00 Y4
If C18 is very long then D1 is very safe.
0.30
0.92

28. Experiment 2
The lateral and longitudinal distances between a nearby vehicle and the host vehicle are decreasing.
Lateral distance between neighboring vehicles
1st
2nd
3rd C9
+6.2%
+0%
C17
+44.7%
C16
+16.4%
-0.7%
-0.6%
C18
+33.9%
+22.8%
Longitudinal distance between neighboring vehicles
172 conditions  172 association rules
Selected Rules
Supp(Y)
Conf(Y) Y1
If C17 is long then D1 is safe.
0.02
1.00 Y2
If C17 is very long then D1 is safe.
0.48
0.82 Y3
If C18 is normal then D1 is safe.
0.16 Y4
If C18 is long then D1 is very safe.
0.17
0.94 Y5
If C18 is very long then D1 is very safe.
0.15 Y6
If C17 is very short and C18 is very short then D1 is very dangerous.
0.05
0.89

29. Conclusions and Future work
Dangerous driving condition analysis system
Fuzzy rough sets attribute reduction
Rule selection
Future work
The data generation system can only generate partial driving conditions of a freeway environment.
The analysis system will need to be continuously improved to match the data from a real vehicle.

30. Thank you for your attention!