Automating Time-series Safety Analysis for Automotive Control Systems Using Weighted Partial Max-SMT

We propose a method to automate the detection of signal disturbance for a given unsafe property. To incorporate a signal disturbance, we introduce an auxiliary variable, called a cushion variable, for each signal variable to store a value altered by the disturbance that causes unintended state transitions. The signal disturbance is defined to negate the equalities between signal variables and their cushion variables. We develop a method to efficiently detect the signal disturbance by using a weighted partial maximum satisfiability modulo theories (Max-SMT) technique as a set of variables altered by faults resulting in an undesirable condition. By assigning the weights properly to the equations, we control the derivation of signal disturbance patterns with the required property. We present an experimental application of our method to a simplified cruise control system as a practical case study in two well-known methods of safety analysis, namely system theoretic process analysis (STPA) and fault tree analysis (FTA), for the automatic detection of time-series signal disturbances.