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The Human Centered Design (HCD) of Partial Autonomous Driver Assistance Systems (PADAS) requires Digital Human Models (DHMs) of human control strategies for simulations of traffic scenarios. The scenarios can be regarded as problem situations with one or more (partial) cooperative problem solvers. According to their roles models can be descriptive or normative . We present new model architectures and applications and discuss the suitability of dynamic Bayesian networks as control models of traffic agents: Bayesian Autonomous Driver (BAD) models. Descriptive  BAD models can be used for simulating human agents in conventional traffic scenarios with Between-Vehicle-Cooperation (BVC) and in new scenarios with In-Vehicle-Cooperation (IVC). Normative BAD models representing error free behavior of ideal human drivers (e.g. driving instructors) may be used in these new IVC scenarios as a first Bayesian approximation or prototype of a PADAS.
 
The Human Centered Design (HCD) of Partial Autonomous Driver Assistance Systems (PADAS) requires Digital Human Models (DHMs) of human control strategies for simulations of traffic scenarios. The scenarios can be regarded as problem situations with one or more (partial) cooperative problem solvers. According to their roles models can be descriptive or normative . We present new model architectures and applications and discuss the suitability of dynamic Bayesian networks as control models of traffic agents: Bayesian Autonomous Driver (BAD) models. Descriptive  BAD models can be used for simulating human agents in conventional traffic scenarios with Between-Vehicle-Cooperation (BVC) and in new scenarios with In-Vehicle-Cooperation (IVC). Normative BAD models representing error free behavior of ideal human drivers (e.g. driving instructors) may be used in these new IVC scenarios as a first Bayesian approximation or prototype of a PADAS.
 
Document type: Part of book or chapter of book
 
 
== Full document ==
 
<pdf>Media:Draft_Content_806027906-beopen988-6397-document.pdf</pdf>
 
  
  
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* [https://link.springer.com/content/pdf/10.1007%2F978-3-642-02809-0_45.pdf https://link.springer.com/content/pdf/10.1007%2F978-3-642-02809-0_45.pdf]
 
* [https://link.springer.com/content/pdf/10.1007%2F978-3-642-02809-0_45.pdf https://link.springer.com/content/pdf/10.1007%2F978-3-642-02809-0_45.pdf]
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: [http://dx.doi.org/10.1007/978-3-642-02809-0_45 http://dx.doi.org/10.1007/978-3-642-02809-0_45] under the license http://www.springer.com/tdm
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* [https://link.springer.com/10.1007/978-3-642-02809-0_45 https://link.springer.com/10.1007/978-3-642-02809-0_45],
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: [https://dblp.uni-trier.de/db/conf/hci/hci2009-11.html#MobusEGZ09 https://dblp.uni-trier.de/db/conf/hci/hci2009-11.html#MobusEGZ09],
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: [http://dx.doi.org/10.1007/978-3-642-02809-0_45 http://dx.doi.org/10.1007/978-3-642-02809-0_45],
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: [https://dl.acm.org/citation.cfm?id=1601814 https://dl.acm.org/citation.cfm?id=1601814],
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: [https://rd.springer.com/chapter/10.1007/978-3-642-02809-0_45 https://rd.springer.com/chapter/10.1007/978-3-642-02809-0_45],
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: [https://academic.microsoft.com/#/detail/1856939722 https://academic.microsoft.com/#/detail/1856939722]

Latest revision as of 15:10, 21 January 2021

Abstract

The Human Centered Design (HCD) of Partial Autonomous Driver Assistance Systems (PADAS) requires Digital Human Models (DHMs) of human control strategies for simulations of traffic scenarios. The scenarios can be regarded as problem situations with one or more (partial) cooperative problem solvers. According to their roles models can be descriptive or normative . We present new model architectures and applications and discuss the suitability of dynamic Bayesian networks as control models of traffic agents: Bayesian Autonomous Driver (BAD) models. Descriptive BAD models can be used for simulating human agents in conventional traffic scenarios with Between-Vehicle-Cooperation (BVC) and in new scenarios with In-Vehicle-Cooperation (IVC). Normative BAD models representing error free behavior of ideal human drivers (e.g. driving instructors) may be used in these new IVC scenarios as a first Bayesian approximation or prototype of a PADAS.


Original document

The different versions of the original document can be found in:

http://dx.doi.org/10.1007/978-3-642-02809-0_45 under the license http://www.springer.com/tdm
https://dblp.uni-trier.de/db/conf/hci/hci2009-11.html#MobusEGZ09,
https://dx.doi.org/10.1007/978-3-642-02809-0_45,
http://dx.doi.org/10.1007/978-3-642-02809-0_45,
https://dl.acm.org/citation.cfm?id=1601814,
https://rd.springer.com/chapter/10.1007/978-3-642-02809-0_45,
https://academic.microsoft.com/#/detail/1856939722
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Published on 01/01/2009

Volume 2009, 2009
DOI: 10.1007/978-3-642-02809-0_45
Licence: CC BY-NC-SA license

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