Assessing the Relation Between Hazards and Variability in Automotive Systems

Safety assessment of automotive systems is highly demanded, as failure of such systems can lead to dramatic consequences. Usually, these systems are affected by some variability as they contain some production parameters (e.g., the car power, or the braking force) that may drastically affect the behaviour of the system, and so the safety guarantees. Moreover, these systems operate in diverse environmental conditions (e.g., dry or slippery road) that may also affect the system behaviour (we name them as environmental parameters). Classical verification/validation techniques perform safety assessment by considering one particular instance of the system in one particular environmental setting. However, they do not assess the influence of system variability on the final safety. In this paper, we propose a framework for assessing the relation of production and environmental parameters with the overall safety. We first propose an approach based on simulation that assigns hazard degrees to partitions of each parameter domain (defined in terms of fuzzy sets). However, the safety could be affected by interactions of different parameters. Therefore, we also propose a clustering approach that aims at identifying patterns of parameter values providing similar hazard degrees. The approaches have been experimented on an industrial case study related to an automotive collision avoidance system implemented in Simulink. Critical parameters and parameter patterns related to potential collisions were identified and explained.