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Kshetrimayum Jevel Singh

Lucky Amesar

Nisha Kanoo

Praful Kambe

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This Project aims towards design of Hybrid Controller using Neuro- Fuzzy technique for Longitudinal controlling of Automotive System

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Introduction

Era of Automation

Automation in vehicle

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Why do we need controller in vehicles?

WHO report on road traffic injury prevention states that

1.2 million people are killed in road accident every year

50 millions are injured

Figure will increase upto 65% in next 20 years

Global cost of road crashes and injuries is about US $ 518 billions per year.

But the basic reason for all this is ……..

“Human error”

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WHO report figure on Road accidents …..

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PROBLEM ANALYSIS

Driving as a continuous decision making process

Earlier speed adaptation systems with limitations

Modified System

HUMANAPPROACH

Efficient in

Decision making

NEURALNETWORKS

FUZZY LOGIC

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NeuralNetwork + Fuzzy

LogicGood for learning.

Not good for human to interpret its internal representation.

• Supervised leaning• Unsupervised learning• Reinforcement learning

Human reasoning scheme.

Fuzzy rules and membership functions are subjective.

• Readable Fuzzy rules• Interpretable

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Cont….

NeuralNetwork + Fuzzy

LogicGood for learning.

Not good for human to interpret its internal representation.

• Supervised leaning• Unsupervised learning• Reinforcement learning

Human reasoning scheme.

Fuzzy rules and membership functions are subjective.

• Readable Fuzzy rules• Interpretable

A Neuro-fuzzy system is a fuzzy system that uses a

learning algorithm derived from or inspired by

neural network theory to determine its parameters

by processing data samples.

A Neuro-fuzzy system is a fuzzy system that uses a

learning algorithm derived from or inspired by

neural network theory to determine its parameters

by processing data samples.

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Vehicle Controller based on “Generic self organizing Fuzzy-Neural

Network” Mapped by

“Yager’s Inference Scheme” It is a fuzzy-Neural network which uses Yager’s inference scheme to interpret fuzzy relations.

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Yager’s Inference Scheme It is an extension of modus pones rule which is

nothing but similar to implication & is also called as affirmative mode

It can be stated as If A is true and

A B then B is true.

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STRUCTURE OF GenSOFNN

Layer 1input

linguistic nodes

Layer 2input term nodes

Layer 3rule

nodes

Layer 4Output term node

Layer 5output

linguistic nodes

1x 2x nx

1y 1y myˆmy

121x 2x nx

1y 1y myˆmy

Layer 1input

linguistic nodes

Layer 2input term nodes

Layer 3rule

nodes

Layer 4Output term node

Layer 5output

linguistic nodes

Fuzzifier

Inference Engine

Defuzzifier

STRUCTURE OF GenSOFNN contd..

131x 2x nx

1y 1y myˆmy

Layer 1input

linguistic nodes

Layer 2input term nodes

Layer 3rule

nodes

Layer 4Output term node

Layer 5output

linguistic nodes

antecedent

consquent

STRUCTURE OF GenSOFNN contd…DIC Technique`

is used

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Adaptation to vehicle controller

GenSoYager GenSoYager

anticipationspeed

Throttle

speed anticipation

Brake

A.) Implementation of Longitudinal Control

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Adaptation to vehicle controller

B.) Training of GenSoYager system

Human Driver Driving Simulator Log File

Visual Feedback

ActionAction

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INPUTS TO VEHICLE CONTROLLER

INPUTS

Speed Anticipation

1. It’s a linear variable.2. It depends upon speed

limit of vehicle

1. It depends upon the curve & distance from curve.2. Calculated by using log file.

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Calculation of anticipation factor

speed distance

Anticipation

algorithm

anticipation

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IMPLEMENTATIONProblem analysis

HUMAN approach

Decision making

GENSOFNN

Training using Error back propagation algorithm & log file

Provided with Fuzzy set of rules to interpret

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ADVANTAGES

Comparatively better control. Anticipation Factor doesn’t vary . Chances of Road mishaps reduces.

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DRAWBACKS

TORCS,an open source simulator which is selected for the simulation.

It doesn’t take into account the action of centripetal force during the car Slipping over a turning.

For this we have to depend upon the reliability of the system to control the vehicle

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Scope of Work

Like longitudinal control lateral control can also be implemented by using the concept of anticipation.

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References M. Peden, R. Scurfield, D. Sleet, et al. World Report

on road traffic injury prvention. World Health Organisation, 2004

M. Pasquier, C. Quek, and M. Toh. Fuzzylot: A Novel self-organising Fuzzy-Neural rule-based pilot system for automated vehicles. Neural networks, vol. 14, no. 8, pp. 1099-1112, Oct. 2001.

W.L.Tung, and C.Quek. GenSoFNN: A Generic self-organising Fuzzy-Neural Network . IEEE Transactions on Neural Networks, vol. 13, no.5, pp.1075-1086, 2002

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