Authors:
Julian Schindler, Frank Köster
Keywords:
multi-driver studies, scenario design, Bayesian networks, probability estimation
Abstract:
When designing driving simulator studies, sometimes high efforts have to be spent to make them successful. Some drivers may not behave as desired, leading to situations unforeseen by the developers. When looking at multi-driver studies, where multiple drivers need to interact with each other in one virtual environment, the probability of performing a successful study is even lower, as the behaviour of the human drivers cannot be fully controlled. While [Oel15b] already proposed guidelines for the creation of such scenarios, this paper describes how the probability of success can be monitored and even enhanced during scenario execution. Therefore, it describes an approach where the probability of success is modelled and where the scenario is dynamically adapted to provide higher rates of success.
Schindler J. and Köster F. A Dynamic and Model-Based Approach for Performing Successful Multi-Driver Studies In: Proceedings of the Driving Simulation Conference 2016 Europe, Driving Simulation Association, Paris, France, 2016, pp. 93-97
Download .txt file
@inproceedings{Schindler2016,
title = {A Dynamic and Model-Based Approach for Performing Successful Multi-Driver Studies},
author = {Julian Schindler and Frank Köster},
editor = {Andras Kemeny and Frédéric Merienne and Florent Colombet and Stéphane Espié},
issn = {0769-0266},
year = {2016},
date = {2016-09-07},
booktitle = {Proceedings of the Driving Simulation Conference 2016 Europe},
pages = {93-97},
address = {Paris, France},
organization = {Driving Simulation Association},
abstract = {When designing driving simulator studies, sometimes high efforts have to be spent to make them successful. Some drivers may not behave as desired, leading to situations unforeseen by the developers. When looking at multi-driver studies, where multiple drivers need to interact with each other in one virtual environment, the probability of performing a successful study is even lower, as the behaviour of the human drivers cannot be fully controlled. While [Oel15b] already proposed guidelines for the creation of such scenarios, this paper describes how the probability of success can be monitored and even enhanced during scenario execution. Therefore, it describes an approach where the probability of success is modelled and where the scenario is dynamically adapted to provide higher rates of success.},
keywords = {Bayesian networks, multi-driver studies, probability estimation, scenario design},
}
Download .bib file
TY - CONF
TI - A Dynamic and Model-Based Approach for Performing Successful Multi-Driver Studies
AU - Schindler, Julian
AU - Köster, Frank
C1 - Paris, France
C3 - Proceedings of the Driving Simulation Conference 2016 Europe
DA - 2016/09/07
PY - 2016
SP - 93
EP - 97
LA - en-US
PB - Driving Simulation Association
SN - 0769-0266
L2 - https://proceedings.driving-simulation.org/proceeding/dsc-2016/a-dynamic-and-model-based-approach-for-performing-successful-multi-driver-studies
ER -
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