Use Information You Have Never Observed Together: Data Fusion as a Major Step Towards Realistic Test Scenarios

Scenario-based testing is a major pillar in the development and effectiveness assessment of automated driving systems. Thereby, test scenarios address different information layers and situations (normal driving, critical situations and accidents) by using different databases. However, the systematic combination of accident and / or normal driving databases into new synthetic databases can help to obtain scenarios that are as realistic as possible. This paper shows how statistical matching (SM) can be applied to fuse different categorial accident and traffic observation databases. Hereby, the fusion is demonstrated in two use cases, each featuring several fusion methods. In use case 1, a synthetic database was generated out of two accident data samples, whereby 78.7% of the original values could be estimated correctly by a random forest classifier. The same fusion using distance-hot-deck reproduced only 67% of the original values, but better preserved the marginal distributions. A real-world application is illustrated in use case 2, where accident data was fused with over 23,000 car trajectories at one intersection in Germany. We could show that SM is applicable to fuse categorial traffic databases. In future research, the combination of hot-deck-methods and machine learning classifiers needs to be further investigated.