REAL TIME RAILWAY DISASTER VULNERABILITY ASSESSMENT AND RESCUE GUIDANCE SYSTEM USING MULTI-LAYERED VIDEO COMPUTATIONAL ANALYTICS

• US 20150009331A1
• Filed: 02/12/2013
• Published: 01/08/2015
• Est. Priority Date: 02/17/2012
• Status: Abandoned Application

• Current Assignee: Balaji Venkatraman
• Original Assignee: Balaji Venkatraman
• Inventors: Venkatraman, Balaji
• Application Number:
  US14/378,326
• Publication Number:
  US 20150009331A1
• Time in Patent Office: Days
• US Class Current: 348/148
• CPC Class Codes: B61L 15/0027 Radio-based, e.g. using GSM-R
• CPC Class Codes: B61L 23/041 Obstacle detection
• CPC Class Codes: B61L 23/042 Track changes detection
• CPC Class Codes: B61L 27/53 for trackside elements or systems, e.g. trackside supervision of trackside control system conditions
• CPC Class Codes: H04N 7/181 for receiving images from a plurality of remote sources

Abstract

The system for real time train disaster vulnerability assessment and rescue guidance using multi layered video computational analytics comprises digital video cameras mounted on train, video cameras mounted at fixed locations on rail route; unmanned aerial monitoring vehicle; train on-broad computer system mounted on train and centralized system centrally located in railway network. The digital video cameras capture video images of railways track and adjacent structure from running train and automatically compute degree of disaster vulnerability from collective analysis of output from all video cameras. In case degree of disaster vulnerability exceeds predetermined threshold value a disaster alert is triggered to take immediate precautionary measures while simultaneously activating On Board Rescue and Response System. In case degree of disaster vulnerability is below predetermined threshold value, the analytics output is transmitted to higher level modules for in-depth advanced analytics by combining real-time train data or real-time geographic and environmental data contributing to a potential disaster.

Claims

1. A system for real time rail disaster vulnerability assessment and rescue guidance using multi-layered video computational analytics, said system comprising:

a. Digital Video Camera System further comprising;

i. plurality of high speed digital video cameras mounted on train for capturing, measuring, computation and performing preliminary analytics of railway track features and adjacent structure using embedded computational video analytics, image processing and artificial neural networks;

ii. plurality of high speed stationary digital video cameras mounted at fixed locations on railway bridges, railway tunnels, railway towers and railway adjacent structure for capturing, measuring, computation and performing preliminary analytics of railway track features and adjacent structure from fixed location using embedded computational video analytics, image processing and artificial neural networks;

iii. high speed digital video camera mounted on unmanned aerial monitoring vehicle for capturing, measuring, computation and performing preliminary analytics of railway track features and adjacent structure using embedded computational video analytics, image processing and artificial neural networks,

wherein said Digital Video Camera System automatically computes degree of disaster vulnerability from collective analysis of output from said video cameras and in case degree of disaster vulnerability exceeds predetermined threshold value, said system triggers a preliminary disaster alert to a Preliminary Automatic Emergency Control System inside the train to take immediate precautionary measures to avoid potential disaster while simultaneously activating On Board Rescue and Response System whereby avoiding the damage to train and occupants and considerably reducing the rescue response time by immediately activating rescue and response system and also transmitting disaster alerts for further confirmation from advanced systems in the network;

b. Train Onboard Computer System mounted inside each train, said Train Onboard Computer System having plurality of high speed computing devices for additionally performing comprehensive and deeper level of advanced analytics on said preliminary analytics output from said Digital Video Camera System using real-time train data contributing to a potential disaster;

wherein said Train Onboard Computer system automatically computes degree of disaster vulnerability from collective analysis of output from said Digital Video Camera System and in case degree of disaster vulnerability exceeds predetermined threshold value, said system triggers an advanced disaster alert to a Advanced Automatic Emergency Control System inside the train to take immediate precautionary measures to avoid potential disaster while simultaneously activating On Board Rescue and Response System and in case degree of disaster vulnerability is below said predetermined threshold value, the advanced analytics output is transmitted to train on board computer system for more detailed centralised analytics whereby performing collective analysis of an abnormality by taking into consideration train related factors as well as historical information of the train from the stored databases contributing to a disaster;

c. Central Command Control Computation System located at a central location in a railway network and connected to said Train Onboard Computer System via communication network, said Central Command Control Computation System having plurality of network connected high speed computing devices configured to additionally perform detailed centralized analytics on said advanced analytics output using real-time geographic and environmental data contributing to a potential disaster,

wherein said Central Command Control Computation System automatically calculates the degree of disaster vulnerability from collective output and triggers a centralised disaster alert to the operator of the train of the risk of an accident involving said train in case degree of disaster vulnerability exceeds threshold value, said Central Command Control Computation System also activates on board rescue and response system upon detecting a serious disaster whereby performing collective analysis of an abnormality by taking into consideration geographic and environmental data contributing to a potential disaster.

2. The system as claimed in claim 1, wherein said video cameras in said Digital Video Camera System are configured to capture real time videos and images of railway track system, railway beds, railway foundations, railway track adjacent structure from a running train.

3. The system as claimed in claim 1, wherein said video cameras in said Digital Video Camera System have embedded analytics components configured to perform preliminary analytics on the track components based on video images captured by said video cameras and are configured to automatically detect the abnormalities in the railway track whereby immediately triggering disaster alert in case of any abnormality detected in the railway track and avoiding a potential disaster instantly.

4. The system as claimed in claim 1, wherein in case degree of disaster vulnerability computed by Digital Video Camera System is below said predetermined threshold value, the preliminary analytics output is transmitted to said Train On Board Computer System for in-depth advanced analytics.

5. The system as claimed in claim 1, wherein said video cameras in said Digital Video Camera System are configured to transmit and exchange data among said Train Onboard Computer System and said Central Command Control Computation System using communication network in the railway network.

6. The system as claimed in claim 1, wherein a simulation system for operational training and education is provided that uses stored video images, said preliminary analytics, said advanced analytics and said centralized analytics from train runs across said railway network for the purpose of training and educational support to railway operational and engineering personnel.

7. The system as claimed in claim 1, wherein said Train Onboard Computer System receives output from said Digital Video Camera System and Train Onboard Computer System mounted on other trains via real time communication network whereby providing intercommunication among all the train in the railway network.

8. The system as claimed in claim 1, wherein in case degree of disaster vulnerability computed by said Digital Video Camera System is below said predetermined threshold value, the output from said preliminary analytics is also sent to emergency maintenance requirement system for rectification of the detected abnormality in the rail route whereby ensuring complete safety of the rail route for future train runs.

9. The system as claimed in claim 1, wherein said real-time train data further comprises:

a. railway track segments inventory database storing data related to segment profiles of various tracks segments;

b. engineering measurement analytics rule engine storing data related to engineering safety specifications;

c. train maintenance and serviceability database storing data related to maintenance and serviceability of the train and associated systems;

d. track maintenance and serviceability master database storing data related to maintenance and serviceability records of railway track and associated systems;

e. railway track segments inventory database storing data related to track segment profiles of various rail route tracks segments such as track position, geographical characteristics, operational characteristics, structural characteristics, environmental characteristics on a given rail route;

f. previous records/analytics outputs from the other trains;

g. train characteristics storing data related to current train speed, vibration in the running train, total number of compartments in the train, data from previous train runs and historical information from the stored database maintained by said Train Onboard Computer System,

whereby considering train related factors contributing to an accident or disaster in addition to track related abnormalities.

10. The system as claimed in claim 1 and claim 9, wherein a real time train vulnerability profile by Train On Board Computer System for each train is computed based on said real time train data wherein said train vulnerability profile is constantly updated depending upon variations in these parameters.

11. The system as claimed in claim 1, wherein a real time passenger vulnerability profile for each occupant inside the train is computed based on the following parameters:

a. seat number of the passenger;

b. medical condition of the passenger;

c. physical condition of the passenger;

d. age and sex of the passenger;

e. train compartment number where the passenger is located;

wherein said passenger vulnerability profile is constantly updated depending upon variations in these parameters.

12. The system as claimed in claim 1, claim 10 and claim 11, wherein said degree of disaster vulnerability by Train On Board Computing System is computed by collective analysis of said advanced analytics, said real time train vulnerability profile and said real time passenger vulnerability profile.

13. The system as claimed in claim 1, wherein in case degree of disaster vulnerability computed by Train Onboard Computer System is below said predetermined threshold value, the output from said advanced analytics is also sent to emergency maintenance requirement system for rectification of the detected abnormality in the rail route for ensuring complete safety of the rail route for future train runs.

14. The system as claimed in claim 1, wherein said real-time geographical and environmental data further comprises real time geographic features prevailing in the rail route and real time environmental and climatic factors prevailing in the rail route, information about other trains in the same rail route having potential to collide with running train whereby combining environmental factors and climatic factors as well as geographical factors to advanced analytics output that may contribute to a potential disaster and subsequently taking necessary precautionary and preventive measures to avoid such disaster.

15. The system as claimed in claim 1, claim 9 and claim 14, wherein a real time train vulnerability profile by central command control computation system for each train is computed based on said real time train data and said real-time geographical and environmental data wherein said train vulnerability profile is constantly updated depending upon variations in the parameters constituting said real time train data and said real-time geographical and environmental data.

16. The system as claimed in claim 1, claim 11 and claim 15, wherein said degree of disaster vulnerability by central command control computation system is computed by collective analysis of said centralised analytics, said real time train vulnerability profile and said real time passenger vulnerability profile.

17. The system as claimed in claim 1, wherein in case degree of disaster vulnerability computed by Central Command Control Computation System is below said predetermined threshold value, the output from said centralised analytics is also sent to centralized emergency maintenance system for rectification of the detected abnormality in the rail route for ensuring complete safety of the rail route for future train runs.

18. The system as claimed in claim 1, wherein said Digital Video Camera System, Train Onboard System and Central Command Control Computation System perform static and dynamic measurements related to railway tracks and perform comparative analysis against pre-defined rail track measurements using computational video analytics, image processing and artificial neural networks and fuzzy logics and exert system algorithms to evaluate the real time railway disaster vulnerability potential of the train.

19. The system as claimed in claim 1, wherein while triggering said disaster alert, the system communicates potential disaster scenarios along with associated impacts immediately to Control Guidance System inside said train and other respective trains in said railway network whereby avoiding further damage to the trains passing through or near the disaster location.

20. The system as claimed in claim 1, wherein upon receiving said disaster alert, said Control Guidance System inside the train automatically transmits alerts to various disaster management agencies of the potential disaster event along with disaster characteristics to enable said disaster management agencies employ appropriate and timely rescue and evacuation operations based on the assessment of the disaster vulnerability.

21. The system as claimed in claim 1, wherein said Onboard Disaster Rescue and Recovery System computes control zones based on impact assessment of disaster vulnerability at different locations on the railway route for providing guidance to avoid and minimize the disaster impact.

22. The system as claimed in claim 21, wherein based on said control zones, said system automatically and dynamically updates the information, including location profile and nature of impending disaster, across all the rail routes in order to alert other trains of the disaster.

23. The system as claimed in claim 1, wherein said Onboard Disaster Rescue and Recovery System activates Response Rescue Functions based on the output from said control zones wherein said Response Rescue Functions further comprise at least one of:

a. application of collision impact reduction buffering system in the train for reducing the impact of sudden break and reducing the harm to the passengers;

b. announcement of the mishappening in all the compartments whereby alerting passengers of the accident and help them take action to rescue themselves;

c. shutting down of the electrical systems inside the train for prevention of fire due to shortcut;

d. activation of infant protection system;

e. automatic breakage of train windows for easy exit from the train;

f. automatic release of emergency outlets for immediate rescue of the passengers from the train;

g. activation of anti skidding friction system inside the train;

h. deployment of air bags for prevention of damage to the passengers from sudden break or movement of train;

i. activation of fire extinguishing system to prevent damage caused due to fire inside the train;

j. application of collision impact reduction system to prevent and reduce damage due to sudden collision;

k. release of first aid kit for providing immediate first aid to wounded passengers.

24. A method for real time rail disaster vulnerability assessment and rescue guidance using video computational analytics, said method comprising steps of:

a. capturing digital video images of railway track of the railway track;

b. performing feature extraction and measurement on said video images of railway track;

c. performing preliminary analytics and reliability analytics on said video images of railway track to identify and classify immediate threat or vulnerability to train;

d. computing degree of disaster vulnerability from collective analysis of preliminary analytics;

i. in case degree of disaster vulnerability exceeds predetermined threshold value, triggering a preliminary disaster alert to take immediate precautionary measures to avoid potential disaster while simultaneously activating On Board Rescue and Response System;

ii. in case degree of disaster vulnerability is below said predetermined threshold value, transmitting said preliminary analytics output to train on board computer system for in-depth advanced analytics,

whereby avoiding the damage to train and occupants and considerably reducing the rescue response time by immediately activating rescue and response system and also transmitting disaster alerts for further confirmation from advanced systems in the network;

e. performing advanced analytics on the output by using said preliminary analytics output and real-time train data contributing to a potential disaster,

f. computing degree of disaster vulnerability from collective analysis of advanced analytics;

i. in case degree of disaster vulnerability exceeds predetermined threshold value, triggering a advanced disaster alert to take immediate precautionary measures to avoid potential disaster while simultaneously activating On Board Rescue and Response System;

ii. in case degree of disaster vulnerability is below said predetermined threshold value, transmitting said advanced analytics output to train on board computer system for centralised analytics,

whereby performing collective analysis of an abnormality by taking into consideration train related factors as well as historical information of the train from the stored databases contributing to a disaster;

g. performing centralised analytics on the output by using said advanced analytics output and real-time geographic and environmental data contributing to a potential disaster;

h. computing degree of disaster vulnerability from collective analysis of centralized analytics and in case degree of disaster vulnerability exceeds predetermined threshold value, triggering centralized disaster alert to take immediate precautionary measures to avoid potential disaster while simultaneously activating On Board Rescue and Response System whereby performing collective analysis of an abnormality by taking into consideration geographic and environmental data contributing to a potential disaster.

25. The method as claimed in claim 24, wherein in case degree of disaster vulnerability from collective analysis of preliminary analytics is below said predetermined threshold value, the output from said preliminary analytics is also sent to emergency maintenance requirement system for rectification of the detected abnormality in the rail route whereby ensuring complete safety of the rail route for future train runs.

26. The method as claimed in claim 24, wherein said real-time train data further comprises:

a. railway track segments inventory database storing data related to segment profiles of various tracks segments;

b. engineering measurement analytics rule engine storing data related to engineering safety specifications;

c. train maintenance and serviceability database storing data related to maintenance and serviceability of the train and associated systems;

d. track maintenance and serviceability master database storing data related to maintenance and serviceability records of railway track and associated systems;

e. railway track segments inventory database storing data related to track segment profiles of various rail route tracks segments such as track position, geographical characteristics, operational characteristics, structural characteristics, environmental characteristics on a given rail route;

f. previous records/analytics outputs from the other trains;

g. train characteristics storing data related to current train speed, vibration in the running train, total number of compartments in the train, data from previous train runs and historical information from the stored database maintained by said Train Onboard Computer System,

whereby considering train related factors contributing to an accident or disaster in addition to track related abnormalities.

27. The system as claimed in claim 24 and claim 26, wherein a real time train vulnerability profile for each train is computed based on said real time train data and said train vulnerability profile is constantly updated depending upon variations in these parameters.

28. The method as claimed in claim 24, wherein said a real time passenger vulnerability profile for each occupant inside the train is computed based on the following parameters:

a. seat number of the passenger;

b. medical condition of the passenger;

c. physical condition of the passenger;

d. age and sex of the passenger;

e. train compartment number where the passenger is located;

f. type of the disaster and the impact magnitude of the disaster,

wherein said passenger vulnerability profile is constantly updated depending upon variations in above parameters.

29. The method as claimed in claim 24, claim 27 and claim 28, wherein said degree of disaster vulnerability by Train On Board Computing System is computed by collective analysis of said advanced analytics, said real time train vulnerability profile and said real time passenger vulnerability profile.

30. The method as claimed in claim 24, wherein in case degree of disaster vulnerability from collective analysis of advanced analytics is below said predetermined threshold value, the output from said advanced analytics is also sent to emergency maintenance requirement system for rectification of the detected abnormality in the rail route whereby ensuring complete safety of the rail route for future train runs.

31. The method as claimed in claim 24, wherein said real-time geographical and environmental data further comprises real time geographic features prevailing in the rail route and real time environmental factors prevailing in the rail route, information about other trains in the same rail route having potential to collide with running train whereby combining environmental factors as well as geographical factors to advanced analytics output that may contribute to a potential disaster and subsequently taking necessary precautionary measures to avoid such disaster.

32. The method as claimed in claim 24, claim 26 and claim 31, wherein a real time train vulnerability profile for each train is computed based on said real time train data and said real-time geographical and environmental data wherein said train vulnerability profile is constantly updated depending upon variations in the parameters constituting said real time train data and said real-time geographical and environmental data.

33. The method as claimed in claim 24, claim 28 and claim 32, wherein said degree of disaster vulnerability by central command control computation system is computed by collective analysis of said centralised analytics, said real time train vulnerability profile and said real time passenger vulnerability profile.

34. The method as claimed in claim 24, wherein in case degree of disaster vulnerability from collective analysis of centralized analytics is below said predetermined threshold value, the output from said centralized analytics is also sent to emergency maintenance requirement system for rectification of the detected abnormality in the rail route whereby ensuring complete safety of the rail route for future train runs.

35. The method as claimed in claim 24, wherein while triggering said disaster alert, the potential disaster scenarios along with associated impacts are communicated immediately to Control Guidance System inside said train and other respective trains in said railway network whereby avoiding further damage to the trains passing through or near the disaster location.

36. The method as claimed in claim 24, wherein while computing said degree of disaster vulnerability, real time train vulnerability profile and real time passenger vulnerability profile is also taken into consideration whereby the system can immediately estimate the necessary precautionary measures to avoid the potential accident or disaster.

37. The method as claimed in claim 24, wherein upon receiving said disaster alert, said Control Guidance System inside the train automatically transmits alerts to various disaster management agencies of the potential disaster event along with disaster characteristics to enable said disaster management agencies employ appropriate and timely rescue and evacuation operations based on the assessment of the disaster vulnerability.

38. The method as claimed in claim 24, wherein in case in case degree of disaster vulnerability exceeds threshold value, control zones are computed based on impact assessment of disaster vulnerability at different locations on the railway route for providing guidance to avoid and minimize the disaster impact.

39. The method as claimed in claim 38, wherein based on said control zones, said system automatically and dynamically updates the information, including location profile and nature of impending disaster, across all the rail routes in order to alert other trains of the disaster.

40. The method as claimed in claim 24, wherein said Onboard Disaster Rescue and Recovery System activates Response Rescue Functions based on the output from said control zones wherein said Response Rescue Functions further comprise at least one of:

a. application of collision impact reduction buffering system in the train for reducing the impact of sudden break and reducing the harm to the passengers;

b. announcement of the mishappening in all the compartments whereby alerting passengers of the accident and help them take action to rescue themselves;

c. shutting down of the electrical systems inside the train for prevention of fire due to shortcut;

d. activation of infant protection system;

e. automatic breakage of train windows for easy exit from the train;

f. automatic release of emergency outlets for immediate rescue of the passengers from the train;

g. activation of anti skidding friction system inside the train;

h. deployment of air bags for prevention of damage to the passengers from sudden break or movement of train;

i. activation of fire extinguishing system to prevent damage caused due to fire inside the train;

j. application of collision impact reduction system to prevent and reduce damage due to sudden collision;

k. release of first aid kit for providing immediate first aid to wounded passengers.