A Dynamic and Model-Based Approach for Performing Successful Multi-Driver Studies
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.
Cite this article
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
@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},
}
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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