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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Year
1890's
1900's
1950's
1970's
1980's
1990's
2000's
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
1900's
1950's
1970's
1980's
1990's
2000's
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.