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Highly automated driving systems have a responsibility to keep a vehicle safe even in abnormal conditions such as random or systematic failures. However, creating redundancy in a system to respond to failures increases the cost of the system, and simple redundancy cannot detect systematic failures because some systematic failures occur in each system at the same time. Systematic failures in automated driving systems cannot be verified sufficiently during the development phase due to numerous patterns of parameters input from outside the system. A safety concept based on a “safety sustainer” for highly automated driving systems is proposed. The safety sustainer is designed for keeping a vehicle in a safe state for several seconds if a failure occurs in the system and notifying the driver that the system is in failure mode and requesting the driver to take over control of the vehicle.

To detect difficult-to-find defects in automotive control systems, we have proposed a modeling method with a program slicing technique. In this method, a verifier adjusts the boundaries of source code to be extracted on a variable dependence graph, in a kind of data flow. We have developed software tools for this method and achieved a 35% decrease in total verification time on model checking. This paper provides some consideration on effective cases of the method from verification practices. There are two types of malfunction causes: one is the timing of processes (race conditions), and the other is complex logics. Each type requires different elements in external environment models. Furthermore, we propose regression verification based on the modeling method above, to further reduce verification time on model checking. The paper outlines tool extensions needed to realize regression verification.