Test Scenario Fusion: How to Fuse Scenarios From Accident and Traffic Observation Data

Scenario-based testing will help validate automated driving systems (ADS) and establish safer road traffic. To date, most data-driven test scenario generation methods rely primarily on one data source such as police accident data (PD), naturalistic driving studies, or video-based traffic observations (VOs). However, none of these data sources perfectly satisfies all layers of the six-layer model for the description of the test scenarios. Moreover, not all available data sources cover the same location and period. Therefore, we fused information from 1648 scenarios extracted from a German VO with information from 74 scenarios extracted from German PD into a comprehensive new PD* database. Finally, PD* consisted of 74 accident scenarios extended, for example, by variables containing the dynamic information of the VO scenarios. Thus, PD* contained more than 350 variables, whereas PD contained only 269 and VO only 122 variables. For the fusion, we followed the Fuse4Rep process model using statistical matching (SM). Subsequently, we derived three logical scenarios from PD* to test an autonomous emergency braking system (AEB) in a stochastic traffic simulation incorporating driver-behavior models. The quality of the fusion itself was satisfactory, and we proposed improving the VO data collection process and observation time to obtain even better results.