Authors:
Marzana Khatun
1
;
Michael Glaß
2
and
Rolf Jung
1
Affiliations:
1
Electrical Engineering,Kempten University of Applied Sciences, Kempten, Bavaria, Germany
;
2
Institute of Embedded Systems/Real-Time Systems, Ulm University, Ulm, Germany
Keyword(s):
HADF, FuSa, SOTIF, Scenario Reduction, Scenario Modeling.
Abstract:
This paper investigates the scenario catalog generation and scenario reduction approaches for a complete Highly Automated Driving Function (HADF). Such approaches focus on the clustering and/or grouping of scenarios by applying a simple stochastic process at an early stage of development. Dealing with an enormous number of scenarios considering Functional Safety (FuSa), Safety Of The Intended Functionality (SOTIF) including cybersecurity desires intelligent approaches for HADF’s scenario reduction. The reduction of scenarios in HADF is a challenge for automotive researchers since it relates to a large number of parameters (like environmental aspects). The main contributions of the scenario generation and reduction approach proposed in this work are the following: (1) contribution to a complete scenario catalog for a dedicated HADF, (2) logical scenario optimization with parameter distribution, and (3) optimize discretization step for finding semi-concrete scenarios that can be execut
ed. Furthermore, the optimization method incorporating the Monte-Carlo (MC) experiment with the CarMaker simulation yields a systematic approach to modeling reduced scenarios without redundancy to support safety.
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