ABSTRACT. One does not need the gift of clairvoyance to predict that in the near future autonomously driving cars will occupy a significant place in our everyday life. In fact, in designated and even public test-drive areas it is reality even now. Autonomous driving comes with the ambition to make road traffic safer, more efficient, more economic, and more comfortable -- and thus to ``make the world a bit better''. Recent accidents with automatic cars resulting in severe injuries and death, however, spark a debate on the safety validation of autonomous driving in general. The biggest challenge for autonomous driving to become a reality is thus most likely not the actual development of intelligent vehicles themselves but their rigorous validation that would justify the necessary level of confidence. It is common sense that classical test approaches are by far not feasible in this emerging area of autonomous driving as these would induce billions of kilometers of real-world driving in each release cycle. To cope with the tremendous complexity of traffic situations that a self-driving vehicle must deal with -- without doing any harm to any other traffic participants -- a promising approach to safety validation is virtual simulation, i.e.\ driving the huge amount of test kilometers in a virtual but realistic simulation environment. A particular interest here is in the validation of the behavior of the autonomous car in rather critical traffic scenarios.

In this position paper, we concentrate on one important aspect in virtual safety validation of autonomous driving, namely on the specification and concretization of critical traffic scenarios. This aspect gives rise to complex and challenging constraint systems to be solved, to which we believe the SC$^2$ community may give essential contributions by their rich variety of diverse methods and techniques.