  The level of autonomous functions in vehicular control systems has been on a
steady rise. This rise makes it more challenging for control system engineers
to ensure a high level of safety, especially against unexpected failures such
as stochastic hardware failures. A generic Backtracking Process Algorithm (BPA)
based on a deductive implementation of the Markov/Cell-to-Cell Mapping
technique is proposed for the identification of critical scenarios leading to
the violation of safety goals. A discretized state-space representation of the
system allows tracing of fault propagation throughout the system, and the
quantification of probabilistic system evolution in time. A case study of a
Hybrid State Control System for an autonomous vehicle prone to a brake-by-wire
failure is constructed. The hazard of interest is collision with a stationary
vehicle. The BPA is implemented to identify the risk significant scenarios
leading to the hazard of interest.
