A Reliability Engineering Based Approach to Model Complex and Dynamic Autonomous Systems

The development of system architectures, fulfilling a diverse set of technical and economic requirements, is known to be a challenging task when designing a new vehicle. The demands particularly concerning the system's reliability, availability, and safety, are, however, remarkably increasing when glancing towards full vehicle autonomy, since this level of automatization excludes any takeover actions by passengers. To satisfy the requirements, a fail-operational system design that includes several fallback paths is required. Since classical approaches, which require adding fallback paths with various redundant and segregated components, contradict the harsh cost constraints prevailing in the automotive sector, further use of those approaches is not desirable. Hence, various new concepts are developed to dissolve this contradiction, i.e., reducing the number of hardware and software components, while on the other hand, keeping the level of reliability high. The problem, though, is that the systems resulting from applying those concepts are highly complex and can not be sufficiently analyzed with today's tools regarding the availability, safety, and reliability of the system. Therefore, in this paper, we introduce AT-CARS (Analyzing Tool for Complex, Autonomous, and Reliable Systems), a tool capable of analyzing various complex systems architectures designed for autonomous vehicles. Our tool aims to support system engineers responsible for determining suitable system architectures that fulfill the expected safety requirements while satisfying the monetary conditions by providing measurements concerning availability, safety, and reliability. Those parameters are determined by a state-based Monte Carlo simulation, which supports dynamic failure management procedures.