Automatic Train Collision and

Accident Avoidance System

By, R.Pradeep Raj III Year EEE, Mepco Schlenk Engineering College.

Contents

• Importance of this topic

• Rail accidents – An overview

• Ideas suggested :

– Automated Braking in Trains

– Real-time Train monitoring

– Alert system using Pattern Recognition

Why this topic..?

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2011-13

Survey on Train Accidents

Rail accidents

• 4 January 1961 - Frontal collision of two passenger trains near Umeshnagar. 35 dead, 61 wounded.

• 13 August 1998: Nineteen killed and 27 injured as a bus rams into the Chennai–Madurai Express train at an unmanned level-crossing.

• 13 November 2013 - A herd of 40 elephants was struck by a passenger train in Chapramari Wildlife Sanctuary.

1. Automated braking

• To detect the presence of obstacle even from a long range

• Human inaccuracy

• Optimal level of brake application

Need :

Braking System

Types :

• Vacuum brake (obsolete) • Air brake (currently in use) - Westinghouse Brake

The average braking distance of a train moving at a speed of 100 km/hr is about 300m.

Dynamic Braking

Westinghouse Brake Lever

Ultrasonic Transducer

Production of high frequency sound waves around

18,000 hertz

Works on the principle of Piezo-electric Effect

Generated by Piezo-electric crystal

Also produced by Magneto-striction effect

Time interval between sending and reception

Generation :

Transceiver :

OBSTACLE

ADC Microcontroller Driver circuit and

Actuator

TRANCEIVER

Application of Brakes

Intimate the Loco-driver

2. Real-time Train Monitoring

Placing for every 300m – 500m in unmanned level crossing and accident prone zones.

It gives the real time parameters of train : Speed of the train Direction of the train Location of the train

Piezo-electric sensor

• Changes in pressure • Acceleration • strain or force

by converting to electrical signals

To measure

Block Diagram

What can we achieve?

• An easiest way to get the real-time parameters

• Intelligent alert system at crossing

• Best way to eradicate Train-Vehicle collision

3. Pattern Recognition

An Application of Digital Image Processing

Based on Bayesian Decision Theory

Types : 1. Statistical Pattern Recognition 2. Structural Pattern recognition

Alert using Pattern Recognition

• It compare the

sensed image with predefined image pattern which is stored and takes decision.

Ecological Gateway

A geographical location along or between the forest where migration of animals occurs.

What we planned ?

• Image pattern of track is predefined in Image processor and monitored regularly.

• If any major change in image pattern is sensed

it will alert personnel in control room.

START

CAMERA MONITORS THE TRACK

IF CHANGE OCCURS

ALERTS THE DETECTION TO CONTROL ROOM

DECISION WAS TAKEN

STOP

NO ACTION (IDLE STATE)

NO

YES

FLOW CHART :

Pros of the Idea

• No need to monitor the track for 24*7

• Animal collision is greatly reduced

• Needs to be installed only in specified area

Conclusion

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1890's

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2011-13

2020's

When these ideas are implemented in the future, surely the rate of rail-accidents due to collision can be drastically reduced.