Möbus, Claus and Eilers, Mark and Zilinski, Malte and Garbe, Hilke (2009) Mixture of Behaviors in a Bayesian Driver Model. In: Der Mensch im Mittelpunkt technischer Systeme. Fortschrittsberichte Mensch-Maschine-Systeme, VDI Reihe 22 (29). VDI-Verlag, Düsseldorf, pp. 221-226. ISBN 978-3-18-302922-8


Volltext (1369Kb)
Official URL: http://shop.vdi-nachrichten.com/buchshop/literatur...


The Human Centered Design (HCD) of Partial Autonomous Driver Assistance Systems (PADAS) requires Digital Human Models (DHMs) of human control strategies for traffic scenario simulations. We present a probabilistic model architecture for generating descriptive models of human driver behavior: Bayesian Autonomous Driver (BAD) models. They implement the sensory-motor system of human drivers in a psychological motivated mixture-of-experts (= mixture-of-schema) architecture with autonomous and goal-based attention allocation processes. Under the assumption of stationarity behavioral processes models are specified across at least two time slices. Learning data are time series of relevant variables: percepts, goals, and actions. We can represent individual or groups of human and artificial agents. Models propagate information in various directions. When working top-down, goals emitted by a cognitive layer select a corresponding expert (schema), which propagates actions, relevance of areas of interest (AoIs) and perceptions. When working bottom-up, percepts trigger AoIs, actions, experts and goals. When the task or goal is defined and the model has certain percepts evidence can be propagated simultaneously top-down and bottom-up and the appropriate expert (schema) and its behavior can be activated. Thus, the model can be easily extended to implement a modified version of the SEEV visual scanning or attention allocation model of Horrey, Wickens, and Consalus. In contrast to Horrey et al. the model can predict the probability of attending a certain AoI on the basis of single, mixed, and even incomplete evidence (goal priorities, percepts, effort to switch between AoIs). In this paper we present the architecture and a proof of concept with plausible but artificial data.

Item Type: Book Section
Uncontrolled Keywords: autonomous and goal-based attention allocation processes, Bayesian autonomous driver model, area-of-interest, SEEV visual scanning or attention allocation 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: 09 Apr 2014 10:03
Last Modified: 09 Apr 2014 10:03
URI: https://oops.uni-oldenburg.de/id/eprint/1835
URN: urn:nbn:de:gbv:715-oops-19160

Actions (login required)

View Item View Item

Document Downloads

More statistics for this item...