Today’s vehicles are evolving towards smart cars, which will be able to drive autonomously and adapt to changing contexts. Incorporating self-adaptation in these cyber-physical systems (CPS) promises great benefits, like cheaper software based redundancy or optimised resource utilisation. As promising as these advantages are, a respective proportion of a vehicle’s functionality poses as safety hazards when confronted with faultand failure situations. Consequently, a system’s safety has to been sured with respect to the availability of multiple software applications, thus often resulting in redundant hardware resources, such as dedicated backup control units. To benefit from self-adaptation by means of creating efficient and safe systems, this work introduces a safety concept in form of a generic adaptation mechanism (GAM). In detail, this generic adaptation mechanism is introduced and analysed with respect to generally known and newly created safety hazards, in order to determine a minimal set of system properties and architectural limitations required to safely perform adaptation. Moreover, the approach is applied to the ICT architecture of a smart e-car, thereby highlighting the soundness, general applicability, and advantages of this safety concept and forming the foundation for the currently ongoing implementation of the GAM within a real prototype vehicle

Abstract. As road vehicles increase their autonomy and the driver reduces his role in the control loop, novel challenges on dependability assessment arise. Model-based design combined with a simulation-based fault injection technique and a virtual vehicle poses as a promising solution for an early safety assessment of automotive systems. To start with, the design, where no safety was considered, is stimulated with a set of fault injection simulations (fault forecasting). By doing so, safety strategies can be evaluated during early development phases estimating the relationship of an individual failure to the degree of misbehaviour on vehicle level. After having decided the most suitable safety concept, a second set of fault injection experiments is used to perform an early safety validation of the chosen architecture. This double-step process avoids late redesigns, leading to significant cost and time savings. This paper presents a simulation-based fault injection approach aimed at finding acceptable safety properties for model-based design of automotive systems. We focus on instrumenting the use of this technique to obtain fault effects and the maximum response time of a system before a hazardous event occurs. Through these tangible outcomes, safety concepts and mechanisms can be more accurately dimensioned. In this work, a prototype tool called Sabotage has been developed to set up, configure, execute and analyse the simulation results. The feasibility of this method is demonstrated by applying it to a Lateral Control system.

As automated driving vehicles become more sophisticated and pervasive, it is increasingly important to assure its safety even in the presence of faults. This paper presents a simulation-based fault injection approach (Sabotage) aimed at assessing the safety of automated vehicle functions. In particular, we focus on a case study to forecast fault effects during the model-based design of a lateral control function. The goal is to determine the acceptable fault detection interval for permanent faults based on the maximum lateral error and steering saturation. In this work, we performed fault injection simulations to derive the most appropriate safety goals, safety requirements, and fault handling strategies at an early concept phase of an ISO 26262-compliant safety assessment process.