1. Intelligent Traffic System
Group.2 Report
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Jin Xie

2. 2
Outline
 Prediction of Vehicle’s Trajectory
 Image Processing
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3. 3
Outline
 Prediction of Vehicle’s Trajectory
 Main Problem
 Fractal: Correlation Dimension
 Machine Learning: Generative Learning Algorithm
 Image Processing
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4. Prediction of Vehicle’s Trajectory
Main Problem
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How to determine the direction the green vehicle will go?

5. Prediction of Vehicle’s Trajectory
Fractal: Correlation Dimension
Chaos Theory
– Correlation dimension (denoted by D), one of the fractal
dimension, is sensitive to the non-linear system behaviors and
reflects the tendency of the non-linear system.
Advantages
– 1. Correlation dimension could be used for extracting signal
features out of a complex non-linear system.
– 2. The output of correlation dimension is simple and intuitive.
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6. Prediction of Vehicle’s Trajectory
Fractal: Correlation Dimension
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Turn left

7. Prediction of Vehicle’s Trajectory
Fractal: Correlation Dimension
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Turn right

8. Prediction of Vehicle’s Trajectory
Fractal: Correlation Dimension
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D = 6.2117
D = 5.3320
Turn left:
Turn right:

9. Prediction of Vehicle’s Trajectory
Fractal: Correlation Dimension
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0
1
2
3
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5
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7
8
9
1 2 3 4 5 6 7 8 9 10
D
Number
D(Turn left) D(Turn right)

10. Prediction of Vehicle’s Trajectory
Fractal: Correlation Dimension
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11. Prediction of Vehicle’s Trajectory
Machine Learning: Generative Learning Algorithm
Use Bayes rule 𝑝 𝑦 𝑥 =
𝑝 𝑥 𝑦 𝑝(𝑦)
𝑝(𝑥)
to derive the posterior
distribution on y given x. Then calculate arg𝑚𝑎𝑥
𝑦
𝑝(𝑦|𝑥) as
the output which represents the value of y in the case where
𝑝(𝑦|𝑥) can obtain maximum value.
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12. Prediction of Vehicle’s Trajectory
Machine Learning: Generative Learning Algorithm
𝑝 𝑦 𝑥 =
𝑝 𝑥 𝑦 𝑝(𝑦)
𝑝(𝑥)
x is the function of data acquired from smart phones and y
represents the prediction result (-1 represents turning left, 0
represents turning right, 1 represents turning right).
 𝑝 𝑥 = 𝑝 𝑥 𝑦 = 0 ∗ 𝑝 𝑦 = 0 + 𝑝 𝑥 𝑦 = 1 ∗ 𝑝 𝑦 = 1 +
𝑝 𝑥 𝑦 = −1 ∗ 𝑝(𝑦 = −1)
 𝑝(𝑥|𝑦) is set to satisfy Gaussian Distribution. The parameters
in the 𝑝(𝑥|𝑦) can be calculated through statistical approach
using the training samples.
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13. Prediction of Vehicle’s Trajectory
Machine Learning: Generative Learning Algorithm
Advantages
– It can use less training samples compared to discriminative learning
algorithm.
– The algorithm complexity is low.
Future work
– Find effective way to solve the problem with the robustness of the
Generative Learning Algorithm.
– How to acquire lane localization data.
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14. 14
Outline
 Prediction of Vehicle’s Trajectory
 Image Processing
 Review
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15. Image Processing
Review
Completed work
– Image matching
– Lane detection (daytime)
Ongoing work
– Compress image
– Lane detection (night)
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16. Thank You