Scenario-based threat metric evaluation based on the highd dataset

Scenario-based approaches have gained popularity in the context of automated vehicles. As in any model-based approach, validation to real data needs to be considered. This work studies concrete scenarios based on real data from a large-scale and open road user trajectory dataset. In particular, we detail on cut-ins and (hard) braking maneuvers and study them based on existing threat metrics. In this work, we present the complete workflow starting from the pre-processing and validation of the data, the definition of concrete scenarios based on how we extract them from the dataset and their evaluation based on several threat metrics. We identify peculiarities of the dataset itself, as well as of the driving behavior of vehicles therein. As this work relies on an open dataset, results can be reproduced and readily compared.