Separably developed functionality as well as increasing situation complexity poses problems for building, testing, and validating future Advanced Driving Assistance Systems (ADAS). These will have to deal with situations in which several current ADAS domains interplay. We argue that a generalized estimation of the future ADAS functions’ benefit is required for efficient testing and evaluations, and propose a quantification based on an estimation of the predicted risk. The approach can be applied to several different types of risks and to such diverse scenarios as longitudinal driving, intersection crossing and lane changes with several traffic participants. Resulting trajectories exhibit a proactive, ”foresighted” driver behavior which smoothly avoids potential future risks.

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