To enable modern cars to drive autonomously or highly automated is one of the most challenging undertakings of our time. To achieve this goal the cars need to comprise their environment and the present traffic situation as exact as possible, while meeting real-time constraints. But that is still not enough – additionally a sufficient prediction of the situation is needed to be able to plan a secure driving corridor. Current Advanced Driver Assistance Systems (ADAS) are already executing and involving such predictions. But most systems, which are currently in the market, are based on simple heuristics and use only the information directly provided by the sensor systems. At this point the PRoTECT project applies with the purpose to improve these prediction accuracies by incorporating also such information a human driver would conclude from the context of the situation and its knowledge thereof. That way, a human driver would e.g. expect a neighboring vehicle to keep a larger distance to its surrounding vehicles or generally speaking to drive more carefully in the case of a slippery road. In addition to that one has to take into account geo-located specialties as crowded roads in India or rectangular roads as in Manhattan. With the thereby improved knowledge about future situations, considerably improvements to the behavior of new ADAS generations in the sense of a humanized driving will become possible.