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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 simulating traffic scenarios. We describe first results to model lateral and longitudinal control behavior of drivers with simple dynamic Bayesian sensory-motor models according to the Bayesian Programming (BP) approach: Bayesian Autonomous Driver (BAD) models. BAD models are learnt from multivariate time series of driving episodes generated by single or groups of users. The variables of the time series describe phenomena and processes of perception, cognition, and action control of drivers. BAD models reconstruct the joint probability distribution (JPD) of those variables by a composition of conditional probability distributions (CPDs). The real-time control of virtual vehicles is achieved by inferring the appropriate actions under the evidence of sensory percepts with the help of the reconstructed JPD.
 
The Human Centered Design (HCD) of Partial Autonomous Driver Assistance Systems (PADAS) requires Digital Human Models (DHMs) of human control strategies for simulating traffic scenarios. We describe first results to model lateral and longitudinal control behavior of drivers with simple dynamic Bayesian sensory-motor models according to the Bayesian Programming (BP) approach: Bayesian Autonomous Driver (BAD) models. BAD models are learnt from multivariate time series of driving episodes generated by single or groups of users. The variables of the time series describe phenomena and processes of perception, cognition, and action control of drivers. BAD models reconstruct the joint probability distribution (JPD) of those variables by a composition of conditional probability distributions (CPDs). The real-time control of virtual vehicles is achieved by inferring the appropriate actions under the evidence of sensory percepts with the help of the reconstructed JPD.
 
Document type: Part of book or chapter of book
 
 
== Full document ==
 
<pdf>Media:Draft_Content_426933696-beopen1025-1241-document.pdf</pdf>
 
  
  
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* [http://oops.uni-oldenburg.de/1841 http://oops.uni-oldenburg.de/1841]
 
* [http://oops.uni-oldenburg.de/1841 http://oops.uni-oldenburg.de/1841]
  
* [http://oops.uni-oldenburg.de/1841/1/Further%2BSteps%2B20090226-PCM.pdf http://oops.uni-oldenburg.de/1841/1/Further%2BSteps%2B20090226-PCM.pdf]
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* [http://www.lks.uni-oldenburg.de/download/Further+Steps+20090226-PCM.pdf http://www.lks.uni-oldenburg.de/download/Further+Steps+20090226-PCM.pdf]
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* [http://link.springer.com/content/pdf/10.1007/978-3-642-02809-0_44 http://link.springer.com/content/pdf/10.1007/978-3-642-02809-0_44],
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: [http://dx.doi.org/10.1007/978-3-642-02809-0_44 http://dx.doi.org/10.1007/978-3-642-02809-0_44] under the license http://www.springer.com/tdm
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* [https://link.springer.com/chapter/10.1007/978-3-642-02809-0_44 https://link.springer.com/chapter/10.1007/978-3-642-02809-0_44],
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: [http://www.uni-oldenburg.de/fileadmin/user_upload/informatik/ag/lks/download/Further+Steps+20090226-PCM.pdf http://www.uni-oldenburg.de/fileadmin/user_upload/informatik/ag/lks/download/Further+Steps+20090226-PCM.pdf],
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: [http://dblp.uni-trier.de/db/conf/hci/hci2009-11.html#MobusE09 http://dblp.uni-trier.de/db/conf/hci/hci2009-11.html#MobusE09],
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: [https://dx.doi.org/10.1007/978-3-642-02809-0_44 https://dx.doi.org/10.1007/978-3-642-02809-0_44],
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: [http://dx.doi.org/10.1007/978-3-642-02809-0_44 http://dx.doi.org/10.1007/978-3-642-02809-0_44],
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: [https://dl.acm.org/citation.cfm?id=1601765.1601813 https://dl.acm.org/citation.cfm?id=1601765.1601813],
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: [https://doi.org/10.1007/978-3-642-02809-0_44 https://doi.org/10.1007/978-3-642-02809-0_44],
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: [http://oops.uni-oldenburg.de/1841/1/Further%2BSteps%2B20090226-PCM.pdf http://oops.uni-oldenburg.de/1841/1/Further%2BSteps%2B20090226-PCM.pdf],
 +
: [https://academic.microsoft.com/#/detail/1873272426 https://academic.microsoft.com/#/detail/1873272426]

Latest revision as of 15:59, 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 simulating traffic scenarios. We describe first results to model lateral and longitudinal control behavior of drivers with simple dynamic Bayesian sensory-motor models according to the Bayesian Programming (BP) approach: Bayesian Autonomous Driver (BAD) models. BAD models are learnt from multivariate time series of driving episodes generated by single or groups of users. The variables of the time series describe phenomena and processes of perception, cognition, and action control of drivers. BAD models reconstruct the joint probability distribution (JPD) of those variables by a composition of conditional probability distributions (CPDs). The real-time control of virtual vehicles is achieved by inferring the appropriate actions under the evidence of sensory percepts with the help of the reconstructed JPD.


Original document

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

http://dx.doi.org/10.1007/978-3-642-02809-0_44 under the license http://www.springer.com/tdm
http://www.uni-oldenburg.de/fileadmin/user_upload/informatik/ag/lks/download/Further+Steps+20090226-PCM.pdf,
http://dblp.uni-trier.de/db/conf/hci/hci2009-11.html#MobusE09,
https://dx.doi.org/10.1007/978-3-642-02809-0_44,
http://dx.doi.org/10.1007/978-3-642-02809-0_44,
https://dl.acm.org/citation.cfm?id=1601765.1601813,
https://doi.org/10.1007/978-3-642-02809-0_44,
http://oops.uni-oldenburg.de/1841/1/Further%2BSteps%2B20090226-PCM.pdf,
https://academic.microsoft.com/#/detail/1873272426
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Published on 01/01/2009

Volume 2009, 2009
DOI: 10.1007/978-3-642-02809-0_44
Licence: CC BY-NC-SA license

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