Scenario-based testing emerges as the main approach to validate automated driving systems (ADS) and thus ensure safe road traffic. Thereby, the test scenarios used should represent the traffic event of the corresponding operational design domain (ODD) and should cover the traffic situation from normal driving to an accident. For this, the fusion of police accident data and video-based traffic observation data into one database for subsequent scenario generation is advisable. Therefore, this paper presents the Fuse4Representativity (Fuse4Rep) process model as part of the Dresden Method, which helps to fuse heterogeneous data sets into one ODD-representative database for lean, fast and comprehensive scenario generation. Hereby, statistical matching is used as the fusion approach building on probable matching variables, such as the 3-digit accident type, the collision type and the misconduct of participants. Moreover, the paper shows how the scenarios generated in this way can be hypothetically used to validate ADS, e.g. in a stochastic traffic simulation incorporating human driver behaviour models. Future studies should apply the Fuse4Rep model in practice and test its validity.
Validating automated driving systems by using scenario-based testing: The Fuse4Rep process model for scenario generation as part of the 'Dresden Method
2022
Zeitschrift für Verkehrssicherheit. Juli 2022(3). ISSN: 0044-3654
Article (Journal)
Electronic Resource
German
DDC: | 380 |
Tema Archive | 2003
|Highly immersive driving simulator for scenario based testing of automated driving functions
Springer Verlag | 2022
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