Abstract
Simulating and predicting behaviour of human drivers with Digital Human Driver Models (DHDMs) has the potential to support designers of new (partially autonomous) driver assistance systems (PADAS) in early stages with regard to understanding how assistance systems affect human driving behaviour. This paper presents the current research on an integrated driver model under development at OFFIS within the EU project ISi-PADAS. We will briefly show how we integrate improvements into CASCaS, a cognitive architecture used as framework for the different partial models which form the integrated driver model. Current research on the driver model concentrates on two aspects of longitudinal control (behaviour a signalized intersections and allocation of visual attention during car following). Each aspect is covered by a dedicated experimental scenario. We show how experimental results guide the modelling process.
Project Integrated Human Modelling and Simulation to support Human Error Risk Analysis of Partially Autonomous Driver Assistance Systems (ISi-PADAS) funded by the European Commission in the 7th Framework Program, Theme 7 Transport FP7-218552.
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Notes
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See [1].
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See [1].
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Misc Functions of the Department of Statistics (e1071), TU Wien (http://cran.r-project.org/web/packages/e1071/index.html).
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Probability prediction is an inherent property of NBCs.
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Note: This could bypassed by using the hyperplane target function as scores.
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Acknowledgments
The research leading to these results has received funding from the European Commission Seventh Framework Program (FP7/2007-2013) under grant agreement no. 218552, Project ISi-PADAS.
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Wortelen, B. et al. (2011). Modelling Aspects of Longitudinal Control in an Integrated Driver Model. In: Cacciabue, P., Hjälmdahl, M., Luedtke, A., Riccioli, C. (eds) Human Modelling in Assisted Transportation. Springer, Milano. https://doi.org/10.1007/978-88-470-1821-1_19
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DOI: https://doi.org/10.1007/978-88-470-1821-1_19
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