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Machine Learning in Intelligent Transportation Systems

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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
Passenger Entertainment
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
Lane Departure
Driver Consciousness
Safty of Vehicle Equipments
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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