Today the automotive industry faces a robust trendtowards assisted and automated driving. The technology toaccomplish this ambition has evolved rapidly over the last fewyears, and yet there are still a lot of algorithmical challengesleft to make an automation of the driving task a safe andcomfortable experience. One of the main remaining challengesis the comprehension of the current traffic situation and theanticipation of all traffic participants’ future driving behavior,which is needed for the technical system to obtain situationawareness; an indispensable foundation for successful decisionmaking.In this paper, a prediction framework is presented that is ableto infer a driver’s maneuver intention. This is achieved via a hybrid Bayesian network whose hidden layers represent a driver’slane contentedness. A pre-training of the network’s parameterswith simulated data provides for human interpretable parameterseven after running the expectation maximization algorithm basedon data gathered on German highways. Moreover, the futuredriving path of any traffic participant is predicted by solving anoptimal control problem, whereby the parameters of the optimalcontrol formulation are found via inverse reinforcement learning.
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