Eilers, Mark and Möbus, Claus (2011) Learning of a Bayesian Autonomous Driver Mixture-of-Behaviors (BAD-MoB) Model. In: Advances in Applied Digital Human Modeling. CRC Press, Taylor & Francis Group, Boca Raton, USA, pp. 436-445. ISBN 978-1-4398-3511-1

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Abstract

The Human or Cognitive Centered Design (HCD) of intelligent transport systems requires computational Models of Human Behavior and Cognition (MHBC). They are developed and used as driver models in traffic scenario simulations and risk- based design. The conventional approach is first to develop handcrafted control-theoretic or artificial intelligence based prototypes and then to evaluate ex post their learnability, usability, and human likeness. We propose a machine-learning alternative: The Bayesian estimation of MHBCs from behavior traces. The learnt Bayesian Autonomous Driver (BAD) models are empirical valid by construction. An ex post evaluation of BAD models is not necessary. BAD models can be built so that they decompose or compose skills into or from basic skills: BAD Mixture-of-Behaviors (BAD MoB) models. We present an efficient implementation which is able to control a simulated vehicle in real-time. It is able to generate complex behaviors of several layers of expertise by mixing and sequencing simpler behavior models.

Item Type:	Book Section
Uncontrolled Keywords:	Bayesian Modeling, Advanced Machine Learning, Bayesian Autonomous Driver Model
Subjects:	Generalities, computers, information > Computer science, internet Philosophy and psychology > Psychology Technology, medicine, applied sciences > Engineering and machine engineering
Divisions:	School of Computing Science, Business Administration, Economics and Law > Department of Computing Science
Date Deposited:	28 Jan 2014 10:50
Last Modified:	28 Jan 2014 10:50
URI:	https://oops.uni-oldenburg.de/id/eprint/1758
URN:	urn:nbn:de:gbv:715-oops-18390
DOI:
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