The recent established goal of autonomous driving cars, motivates the discussion about safety relevant performance parameters in the automotive industry. The majority of currently accepted key performance indicators (KPIs) do not allow a good prediction over the system performance along a safety relevant critical effect chain. A breakdown of the functional system down to component and sensor levels makes this KPI problem evident. We will present a methodology for sensor performance prediction by a probabilistic approach, on the basis of significant critical use cases. As a result the requirement engineering along the effect chain especially for safety relevant processes appears transparent and understandable. Specific examples from the field of image quality will concentrate on the proposal of a new KPI, the contrast detection probability (CDP). This proposal is currently under discussion within the P2020 work group on automotive image quality and challenges known KPIs such as SNR, especially with respect their effects on automotive use cases.