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Machine Learning in Intelligent Transportation Systems
Transcript of Machine Learning in Intelligent Transportation Systems
Sensors inside and outside
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
Safty of Vehicle Equipments
Choose the Best Action to be Done
Percentage of Situations that the Best Action is Choosen
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
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?