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Recently, multiple naturalistic traffic datasets of human-driven trajectorieshave been published (e.g., highD, NGSim, and pNEUMA). These datasets have beenused in studies that investigate variability in human driving behavior, forexample for scenario-based validation of autonomous vehicle (AV) behavior,modeling driver behavior, or validating driver models. Thus far, these studiesfocused on the variability on an operational level (e.g., velocity profilesduring a lane change), not on a tactical level (i.e., to change lanes or not).Investigating the variability on both levels is necessary to develop drivermodels and AVs that include multiple tactical behaviors. To expose multi-levelvariability, the human responses to the same traffic scene could beinvestigated. However, no method exists to automatically extract similar scenesfrom datasets. Here, we present a four-step extraction method that uses theHausdorff distance, a mathematical distance metric for sets. We performed acase study on the highD dataset that showed that the method is practicallyapplicable. The human responses to the selected scenes exposed the variabilityon both the tactical and operational levels. With this new method, thevariability in operational and tactical human behavior can be investigated,without the need for costly and time-consuming driving-simulator experiments.

Uncovering variability in human driving behavior through automatic extraction of similar traffic scenes from large naturalistic datasets