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.
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:90010 |
| Date | 20 February 2024 |
| Creators | Bäumler, Maximilian, Prokop, Günther |
| Contributors | Technische Universität Dresden |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | German |
| Detected Language | English |
| Type | info:eu-repo/semantics/publishedVersion, doc-type:article, info:eu-repo/semantics/article, doc-type:Text |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | 0044-3654, 10.53184/ZVS3-2022-11, 0044-3654, urn:nbn:de:bsz:14-qucosa2-825568, qucosa:82556 |
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