google car(autonomous car)

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PRESENTED BY SACHIN KS S7 ECE ROLL NO. 34

GOOGLE CAR

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CONTENTS

INTRODUCTION

TECHNOLOGIES

SAFETY

ADVANTAGES

LIMITATIONS

CONCLUSION

REFERENCE

INTRODUCTION

The Google Driverless car is a project by Google .

Developing technology for autonomous cars also known as TESLA car.

The Google Driverless Car is like any car, but:

It can steer itself while looking out for obstacles.

It can accelerate itself to the correct speed limit.

It can stop and go itself based on any traffic condition. 

 The software powering Google's cars is called Google Chauffeur.

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TECHNOLOGIES

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Laser range finder

Front Camera

Bump mounted Radars

Aerial

Ultrasonic Sensors

Combination of sensors

Mapping in advance

Route finding

Astonishing technologies that power Google self-driving car :

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1.LASER RANGE FINDER LIDAR (Light Detection and Ranging) is an optical remote sensing technology .

Also called LADAR (Laser Imaging Detection and Ranging).

Measure the distance & obstacles by illuminating the target with laser light and analyzing.

It use an array of 64 laser beams & camera creates 3D images for obstacle detection.

Measures displacement by using laser beams to hit the object and come back.

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Cont..

The module resides inside a rotating drum.

High-speed computer processing,

The vehicle can create a real-time experience.

Virtual map of the obstacles can be displayed.

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2.FRONT CAMERA’S

Used for near vision

Mounted on the windshield helping the car ‘see’ objects right in front of it.

Detects and record information about road signs and traffic lights, which is intelligently interpreted by the car’s in built software

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3.RADARS Four Radars are mounted on the car’s front and rear bumpers.

Enables the car to be aware of vehicles in front of it and behind it.

The radar sensor on the car’s bumpers keeps a ‘digital eye’ on the car ahead.

The software is programmed to (at all times) maintain a distance of 2-4 metre

Google’s self driving cars use this technology to keep passengers and other motorists safe by avoiding bumps and crashes

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4.AERIAL Car receives information about the precise location of the car, with the help of GPS Satellites.

As the vehicle moves, the vehicle’s internal map is updated with new positional information displayed by the sensors.

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5.ULTRASONIC SENSORS

Keep track of the movements of the car and will alert the car about the obstacles in the rear.

These ultrasonic sensors are ready in action in some of the technologically advanced cars today.

Car that offers automatic ‘Reverse Park Assist’ technology utilize such sensors to help navigate that car into tight reverse parking spots.

These sensors get activated when the car is engaged in the reverse gear.

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6.COMBINATION OF SENSORS All the data gathered by these sensors is collected and interpreted together by the car’s CPU or inbuilt software system to create a safe driving experience.

7.MAPPING It maps out the route and it’s road conditions including poles, road markers , road signs and more.

As the car moves , Laser range finder kicks in and generates a detailed 3D map of the environment at that moment.

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8.ROUTE FINDING

Path planning

Path generation

Path tracking

Path recording

Autonomous vehicle

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SAFETY

The car itself is limited to 25 mph

Emergency stop button that passengers can hit at anytime

The car will wait a second after the traffic lights turn green before it moves off

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ADVANTAGES Fewer traffic collisions

Reduction in the need for traffic police

Smoother ride

Removal of the steering wheel

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LIMITATIONS

The LIDAR technology cannot spot some potholes or discern when humans , such as a police officer, are signaling the car to stop.

Unauthorized parties, hackers, or even terrorists could capture data, alter records.

Instigate attacks on systems, compromise passenger privacy by tracking individual vehicles, or identify residences.

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INTERIOR VIEW

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CONCLUSION

• Human safety

• Better traffic conditions

• Accident rate can decrease

• No need of drivers

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REFERENCE

[1] Aaron J. Dalton, Autonomous path planning with remote sensing data, 2008 [2] GeeksforGeeks, Algorithms for shortest path, 2011

[3] Johann Borenstein, Optimal Path algorithm for Autonomous Vehicles, Vol. 16, 2012, pp. 297-309

[4] Anel A. Montes, Network Shortest path application for optimum track ship routing, Kindle Edition, 2012   

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