Automatic Train Collision and Accidence Avoidance system
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Transcript of Automatic Train Collision and Accidence Avoidance system
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Contents
• Importance of this topic
• Rail accidents – An overview
• Ideas suggested :
– Automated Braking in Trains
– Real-time Train monitoring
– Alert system using Pattern Recognition
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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
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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.
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1. Automated braking
• To detect the presence of obstacle even from a long range
• Human inaccuracy
• Optimal level of brake application
Need :
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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
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Westinghouse Brake Lever
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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
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Generation :
Transceiver :
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OBSTACLE
ADC Microcontroller Driver circuit and
Actuator
TRANCEIVER
Application of Brakes
Intimate the Loco-driver
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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
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Piezo-electric sensor
• Changes in pressure • Acceleration • strain or force
by converting to electrical signals
To measure
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Block Diagram
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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
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3. Pattern Recognition
An Application of Digital Image Processing
Based on Bayesian Decision Theory
Types : 1. Statistical Pattern Recognition 2. Structural Pattern recognition
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Alert using Pattern Recognition
• It compare the
sensed image with predefined image pattern which is stored and takes decision.
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Ecological Gateway
A geographical location along or between the forest where migration of animals occurs.
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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.
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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 :
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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
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Conclusion
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1890's
1900's
1950's
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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.
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