Reliability and dependability of software in modern cars is of outmost importance. Predicting these properties for software under development is therefore important for modern car OEMs, and using reliability growth models (e.g. Rayleigh, Goel-Okumoto) is one approach. In this paper we evaluate a number of standard reliability growth models on a real software system from automotive industry. The results of the evaluation show that models can be fitted well with defect inflow data but certain parameters need to be adjusted manually in order to predict reliability more precisely in late test phases. In this paper we provide recommendations for how to adjust the models and how the adjustments should be used in the development process of software in the automotive domain by investigating data from an industrial project.