Eilers, Mark and Möbus, Claus (2011) Learning the Human Longitudinal Control Behavior with a Modular Hierarchical Bayesian Mixture-of-Behaviors Model. In: IEEE Intelligent Vehicles Symposium. INTELLIGENT VEHICLES SYMPOSIUM . Curran Associates Inc, Red Hook, NY, USA, pp. 540-545. ISBN 978-1-4577-0890-9

Full text not available from this repository. (Request a copy)
Official URL: http://www.proceedings.com/12022.html


Modeling drivers' behavior is believed to be essential for the rapid prototyping of error-compensating assistance systems. Various authors proposed control-theoretic and production-system models. These models are handcrafted in a top-down software engineering process. Here we propose a machine-learning alternative by estimating stochastic driver models from behavior traces. They are more robust than their non-stochastic predecessors. In this paper we present a Bayesian Autonomous Driver Mixture-of-Behaviors (BAD MoB) model for the longitudinal control of human drivers in an inner-city traffic scenario. It is learnt on the basis of multivariate time-series obtained in simulator studies. Percepts relevant for longitudinal control were included in the model by a structure-learning method using Bayesian information criteria. Besides mimicking human driver behavior we suggest using the model for prototyping intelligent assistance systems with human-like behavior.

Item Type: Book Section
Uncontrolled Keywords: Longitudinal control, Bayesian Model, Bayesian Control, Bayesian Autonomous Driver Model, Mixture of behavior 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: 06 Feb 2014 08:47
Last Modified: 15 Oct 2015 08:42
URI: https://oops.uni-oldenburg.de/id/eprint/1775
URN: urn:nbn:de:gbv:715-oops-18563
DOI: 10.1109/IVS.2011.5940530

Actions (login required)

View Item View Item