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A concept for time-related forecasts of lane change maneuvers in highway scenarios is presented within the present work. Automated driving systems rely on understanding the driving environment to fulfill their driving task transparently and safely. This involves the perception of the driving environment as well as its interpretation to detect and predict driving maneuvers of road users.

Fahrstreifenwechsel --- Automatisches Fahren --- Maschinelles Lernen --- dynamic Bayesian networks --- Dynamische Bayes'sche Netzwerke --- lane change --- machine learning --- automated driving