Markov models are a promising tool regarding the assessment of availability, safety, security, and reliability of autonomous driving functions. The paper addresses challenges regarding the overall system functional and static modeling and related overall Markov diagram design options. To this end, the model space is presented, extending the main functions consisting of extended sensory system, decision and control, and vehicle platform manipulation. Sample transition models from literature are used. It is shown how to color-label overall Markov system product states in terms of the level of their criticality, independent of the multiplicity of failures. This is used to model the effect of structural and functional redundancies, e.g., of redundant sensors and sensors of different technology. The modeling approach allows to compare the effect of redundancy options on a systemic level, as well as to identify the need for further aggregation or subdivision of Markov states or refinement of the transition modeling and simulation approach. For instance, in terms of providing statistical assessment of historic events or by using simulation results of specific autonomous driving scenarios, e.g., interaction with vulnerable road users in case of darkness, bad weather, and partial sensor degradation. The paper presents Markov modeling results with a focus on modeling of redundancies of sensors.