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tree of possible runs for each set of scenario parameters. Each node in the
tree stands for a probabilistic decision, and each edge is labelled accordingly
by a probability. By its nature, this random tree is accessible (only) in a
top-down fashion. To explore it, paths are taken systematically. The branch
probabilities encountered along the way are multiplied to compute the path
probability. If a path probability gets below a minimum threshold fixed at
the beginning of the procedure, its further exploration is stopped. Completed
runs yield values for the property of interest. These are used to annotate the
branches which have been taken with estimations of (maximal) property val-
ues and reliability information, guiding the further exploration of the tree.
An implementation of this exploration procedure is currently under de-
velopment, but still in an experimental state. Due to intricacies of the driver
model and the simulation environment, further stabilization is needed to get
a procedure yielding highly valid and dependable estimations.
6
Summary
We have presented a way of exploring via simulation the functionality of assis-
tance systems and their effect on safety, given executable behaviour models of
the driver and all other constituents of the scenario. The presented criticality-
guided simulation explores the complex model and provides the designer with
meaningful information on potentially dangerous situations arising from the
current ADAS design and thus increases the quality. Our results on the pre-
sented case study indicate that this approach may indeed be helpful to reduce
the number of tests with human subjects. The techniques are yet to be ex-
plored on a larger scale, which we intend to do in the near future. Also we
will develop and test further techniques for speeding up the simulation and
guaranteeing a high reliability of the resulting assessment. In particular, we
will use the information about the internal states of driver and ADAS model
for coverage and guidance.
Acknowledgements: We acknowledge the many fruitful discussions and
in particular the work of the other participants in the IMoST project and
further cooperating projects which provided the models whose behaviour we
have set out to explore.
References
1. M. Baumann, H. Colonius, H. Hungar, F. Köster, M. Langner, A. Lüdtke,
C. Möbus, J. Peinke, S. Puch, C. Schiessl, R. Steenken, and L. Weber. Integrated
modelling for safe transportation - driver modeling and driver experiments, In:
Fahrermodellierung in Wissenschaft und Wirtschaft, 2. Berliner Fachtagung für
Fahrzeugsmodellierung. VDI Verlag, Düesseldorf, 2008.
2. L. de Alfaro, M. Faella, and M. I. A. Stoelinga. Linear and branching metrics for
quantitative transition systems. In
Proc. 31st Int'l Colloq. on Automata, Lan-
guages and Programming (ICALP04), Turku, Finland
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Lecture
Notes in Computer Science
, pages 97-109, Berlin, 2004. Springer.
