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DTSTART:20190331T030000
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DTSTART:20191027T020000
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BEGIN:VEVENT
DTSTAMP:20190429T214119Z
UID:9EDC850F-1A46-417A-9CAC-F48BC822AC01
DTSTART;TZID=Europe/Zurich:20190405T173000
DTEND;TZID=Europe/Zurich:20190405T193000
DESCRIPTION:Standard Guidance\, Navigation\, and Control (GN&C) systems tak
 e state data from a navigation system and create a trajectory that minimiz
 es some a-priori determined cost function. These cost functions are typica
 lly time\, money\, weight\, or any general physically realizable quantity.
  Previous work has been done to show the effectiveness of using risk as th
 e sole objective function. However\, this previous work used Poisson distr
 ibutions and historical estimates to achieve this goal. In this talk we pr
 esent the SRA (Situation Risk Assessment) method contained within the inte
 lligent collision avoidance (iSC) platform. The SRA method uses data clust
 ering\, and pattern re-cognition to create a historically based estimate o
 f guidance probabilities. These are then used in data driven\, dynamic mod
 els to create the fu-ture probability fields of the situation. This probab
 ility\, along with the other agent’s goals and objectives\, are then use
 d to create a minimum risk guidance solution nautical situations and for a
 ir traffic.\n\nCo-sponsored by: ION-CH\n\nSpeaker(s): Edwin William\, \n\n
 Agenda: \nStandard Guidance\, Navigation\, and Control (GN&C) systems take
  state data from a navigation system and create a trajectory that minimize
 s some a-priori determined cost function. These cost functions are typical
 ly time\, money\, weight\, or any general physically realizable quantity. 
 Previous work has been done to show the effectiveness of using risk as the
  sole objective function. However\, this previous work used Poisson distri
 butions and historical estimates to achieve this goal. In this talk we pre
 sent the SRA (Situation Risk Assessment) method contained within the intel
 ligent collision avoidance (iSC) platform. The SRA method uses data cluste
 ring\, and pattern re-cognition to create a historically based estimate of
  guidance probabilities. These are then used in data driven\, dynamic mode
 ls to create the fu-ture probability fields of the situation. This probabi
 lity\, along with the other agent’s goals and objectives\, are then used
  to create a minimum risk guidance solution nautical situations and for ai
 r traffic.\n\nRoom: D 1.1\, Bldg: HG \, ETH Zürich\, ZURICH\, Switzerland
 \, Switzerland\, 8000
LOCATION:Room: D 1.1\, Bldg: HG \, ETH Zürich\, ZURICH\, Switzerland\, Swi
 tzerland\, 8000
ORGANIZER:heinz_wipf@bluewin.ch
SEQUENCE:0
SUMMARY:Minimum Risk Guidance Solutions Using Data Driven Probabilities
URL;VALUE=URI:https://events.vtools.ieee.org/m/198414
X-ALT-DESC:Description: <br /><p>Standard Guidance\, Navigation\, and Contr
 ol (GN&amp\;C) systems take state data from a navigation system and create
  a trajectory that minimizes some a-priori determined cost function. These
  cost functions are typically time\, money\, weight\, or any general physi
 cally realizable quantity. Previous work has been done to show the effecti
 veness of using risk as the sole objective function. However\, this previo
 us work used Poisson distributions and historical estimates to achieve thi
 s goal. In this talk we present the SRA (Situation Risk Assessment) method
  contained within the intelligent collision avoidance (iSC) platform. The 
 SRA method uses data clustering\, and pattern re-cognition to create a his
 torically based estimate of guidance probabilities. These are then used in
  data driven\, dynamic models to create the fu-ture probability fields of 
 the situation. This probability\, along with the other agent&rsquo\;s goal
 s and objectives\, are then used to create a minimum risk guidance solutio
 n nautical situations and for air traffic.</p><br /><br />Agenda: <br /><p
 >Standard Guidance\, Navigation\, and Control (GN&amp\;C) systems take sta
 te data from a navigation system and create a trajectory that minimizes so
 me a-priori determined cost function. These cost functions are typically t
 ime\, money\, weight\, or any general physically realizable quantity. Prev
 ious work has been done to show the effectiveness of using risk as the sol
 e objective function. However\, this previous work used Poisson distributi
 ons and historical estimates to achieve this goal. In this talk we present
  the SRA (Situation Risk Assessment) method contained within the intellige
 nt collision avoidance (iSC) platform. The SRA method uses data clustering
 \, and pattern re-cognition to create a historically based estimate of gui
 dance probabilities. These are then used in data driven\, dynamic models t
 o create the fu-ture probability fields of the situation. This probability
 \, along with the other agent&rsquo\;s goals and objectives\, are then use
 d to create a minimum risk guidance solution nautical situations and for a
 ir traffic.</p>
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