  The complex functional structure of driverless vehicles induces a multitude
of potential malfunctions. Established approaches for a systematic hazard
identification generate individual potentially hazardous scenarios for each
identified malfunction. This leads to inefficiencies in a purely expert-based
hazard analysis process, as each of the many scenarios has to be examined
individually. In this contribution, we propose an adaptation of the strategy
for hazard identification for the development of automated vehicles. Instead of
focusing on malfunctions, we base our process on deviations from desired
vehicle behavior in selected operational scenarios analyzed in the concept
phase. By evaluating externally observable deviations from a desired behavior,
we encapsulate individual malfunctions and reduce the amount of generated
potentially hazardous scenarios. After introducing our hazard identification
strategy, we illustrate its application on one of the operational scenarios
used in the research project UNICAR$agil$.
