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Machine Learning in Intelligent Transportation Systems

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by

Besat Zardosht

on 20 April 2013

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Transcript of Machine Learning in Intelligent Transportation Systems

21.037829 -89.74433507 -120.416029 Input 1 Hidden 1 60.9672331 27.62416177 Output 1 Machine Learning In Intelligent Transportation Sysytems Besat Zardosht Charles X Ling Intelligent Transportation Systems Machine Learning Bachpropagation Algorithm 10-Fold Cross Validation Thank You Navigation
Communication
Passenger Entertainment
Safe
Efficient Direction
Visual Map
Sensors inside and outside
Cruise Control
System Parking
Connection with other Vehicles VENIS Simulation Venis: Inter Vehicular Communication Simulation Framework Target Attribute
Warn the Driver
Control the Vehicle Input Attributes
Distance from Vehicle in front
Time
Lane Departure
Driver Consciousness
Safty of Vehicle Equipments
Accident
Collision
Weather Task:
Choose the Best Action to be Done
Preformance Measurement:
Percentage of Situations that the Best Action is Choosen
Training Examples:
Simplified Data from Venis Simulation ANN is well suited for:
Noisy Training Data
Complex Sensor Data
Learning Time is Not Important Learn Weights for Multilayer Network μ: 0.05
Ni: 8
Nh: 11
No: 2 The Number of Examples are Small:
Use 10-Fold Cross Validation Number of Possible examples: 1440
Number of Examples: 200
K in K-Fold Cross Validation: 10 Weights Errors by: under supervision of: Nov 30 The University of Western Ontario Any Question?
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