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Dynamic Probability Fields for Risk Assessment and Guidance Solutions
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| Zeitschriftentitel: | Annual of Navigation |
|---|---|
| Personen und Körperschaften: | , |
| In: | Annual of Navigation, 26, 2019, 1, S. 33-45 |
| Format: | E-Article |
| Sprache: | Unbestimmt |
| veröffentlicht: |
Walter de Gruyter GmbH
|
| Schlagwörter: |
| author_facet |
Williams, Edwin Jin, Yan Williams, Edwin Jin, Yan |
|---|---|
| author |
Williams, Edwin Jin, Yan |
| spellingShingle |
Williams, Edwin Jin, Yan Annual of Navigation Dynamic Probability Fields for Risk Assessment and Guidance Solutions Earth-Surface Processes |
| author_sort |
williams, edwin |
| spelling |
Williams, Edwin Jin, Yan 2300-6633 1640-8632 Walter de Gruyter GmbH Earth-Surface Processes http://dx.doi.org/10.1515/aon-2019-0004 <jats:title>Abstract</jats:title> <jats:p>Standard Guidance, Navigation, and Control (GN&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 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 distributions and historical estimates to achieve this goal. In this paper we present the situation-risk assessment (SRA) method contained within the intelligent situation assessment and collision avoidance (iSC) platform. The SRA method uses data clustering, and pattern recognition to create a historically based estimate of guidance probabilities. These are then used in data driven, dynamic models to create the future probability fields of the situation. This probability, along with the other agent’s goals and objectives, are then used to create a minimum risk guidance solution in the nautical environment.</jats:p> Dynamic Probability Fields for Risk Assessment and Guidance Solutions Annual of Navigation |
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Walter de Gruyter GmbH, 2019 |
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Walter de Gruyter GmbH, 2019 |
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2300-6633 1640-8632 |
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Walter de Gruyter GmbH |
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Annual of Navigation |
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49 |
| title |
Dynamic Probability Fields for Risk Assessment and Guidance Solutions |
| title_unstemmed |
Dynamic Probability Fields for Risk Assessment and Guidance Solutions |
| title_full |
Dynamic Probability Fields for Risk Assessment and Guidance Solutions |
| title_fullStr |
Dynamic Probability Fields for Risk Assessment and Guidance Solutions |
| title_full_unstemmed |
Dynamic Probability Fields for Risk Assessment and Guidance Solutions |
| title_short |
Dynamic Probability Fields for Risk Assessment and Guidance Solutions |
| title_sort |
dynamic probability fields for risk assessment and guidance solutions |
| topic |
Earth-Surface Processes |
| url |
http://dx.doi.org/10.1515/aon-2019-0004 |
| publishDate |
2019 |
| physical |
33-45 |
| description |
<jats:title>Abstract</jats:title>
<jats:p>Standard Guidance, Navigation, and Control (GN&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 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 distributions and historical estimates to achieve this goal. In this paper we present the situation-risk assessment (SRA) method contained within the intelligent situation assessment and collision avoidance (iSC) platform. The SRA method uses data clustering, and pattern recognition to create a historically based estimate of guidance probabilities. These are then used in data driven, dynamic models to create the future probability fields of the situation. This probability, along with the other agent’s goals and objectives, are then used to create a minimum risk guidance solution in the nautical environment.</jats:p> |
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| author | Williams, Edwin, Jin, Yan |
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| description | <jats:title>Abstract</jats:title> <jats:p>Standard Guidance, Navigation, and Control (GN&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 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 distributions and historical estimates to achieve this goal. In this paper we present the situation-risk assessment (SRA) method contained within the intelligent situation assessment and collision avoidance (iSC) platform. The SRA method uses data clustering, and pattern recognition to create a historically based estimate of guidance probabilities. These are then used in data driven, dynamic models to create the future probability fields of the situation. This probability, along with the other agent’s goals and objectives, are then used to create a minimum risk guidance solution in the nautical environment.</jats:p> |
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| imprint | Walter de Gruyter GmbH, 2019 |
| imprint_str_mv | Walter de Gruyter GmbH, 2019 |
| institution | DE-D275, DE-Bn3, DE-Brt1, DE-D161, DE-Zwi2, DE-Gla1, DE-Zi4, DE-15, DE-Pl11, DE-Rs1, DE-105, DE-14, DE-Ch1, DE-L229 |
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| physical | 33-45 |
| publishDate | 2019 |
| publishDateSort | 2019 |
| publisher | Walter de Gruyter GmbH |
| record_format | ai |
| recordtype | ai |
| series | Annual of Navigation |
| source_id | 49 |
| spelling | Williams, Edwin Jin, Yan 2300-6633 1640-8632 Walter de Gruyter GmbH Earth-Surface Processes http://dx.doi.org/10.1515/aon-2019-0004 <jats:title>Abstract</jats:title> <jats:p>Standard Guidance, Navigation, and Control (GN&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 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 distributions and historical estimates to achieve this goal. In this paper we present the situation-risk assessment (SRA) method contained within the intelligent situation assessment and collision avoidance (iSC) platform. The SRA method uses data clustering, and pattern recognition to create a historically based estimate of guidance probabilities. These are then used in data driven, dynamic models to create the future probability fields of the situation. This probability, along with the other agent’s goals and objectives, are then used to create a minimum risk guidance solution in the nautical environment.</jats:p> Dynamic Probability Fields for Risk Assessment and Guidance Solutions Annual of Navigation |
| spellingShingle | Williams, Edwin, Jin, Yan, Annual of Navigation, Dynamic Probability Fields for Risk Assessment and Guidance Solutions, Earth-Surface Processes |
| title | Dynamic Probability Fields for Risk Assessment and Guidance Solutions |
| title_full | Dynamic Probability Fields for Risk Assessment and Guidance Solutions |
| title_fullStr | Dynamic Probability Fields for Risk Assessment and Guidance Solutions |
| title_full_unstemmed | Dynamic Probability Fields for Risk Assessment and Guidance Solutions |
| title_short | Dynamic Probability Fields for Risk Assessment and Guidance Solutions |
| title_sort | dynamic probability fields for risk assessment and guidance solutions |
| title_unstemmed | Dynamic Probability Fields for Risk Assessment and Guidance Solutions |
| topic | Earth-Surface Processes |
| url | http://dx.doi.org/10.1515/aon-2019-0004 |