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With increasing levels of driving automation, the perception provided by automotive environment sensors becomes highly safety relevant. A correct assessment of the sensors’ perception reliability is therefore crucial for ensuring the safety of the automated driving functionalities. There are currently no standardized procedures or guidelines for demonstrating the perception reliability of the sensors. Engineers therefore face the challenge of setting up test procedures and plan test drive efforts. Null Hypothesis Significance Testing has been employed previously to answer this question. In this contribution, we present an alternative method based on Bayesian parameter inference, which is easy to implement and whose interpretation is more intuitive for engineers without a profound statistical education. We show how to account for different environmental conditions with an influence on sensor performance and for statistical dependence among perception errors.

In ride comfort as well as driving dynamics, the behavior of the vehicle is affected by several subsystems and their properties. When analyzing the suspension, especially the characteristics of the main spring and damper but also rubber bushings are of main importance. Still, the properties of the different components are dependent on the present operating conditions. Concerning rubber bushings, several effects have already been investigated, e.g. dependencies of the transfer function of frequency, amplitude or load history. In this context influences of changes in temperature are often neglected. However, in the following research, the focus specifically lies on determination and analysis of the temperature dependency of rubber bushings. For this purpose, initially the relationship between properties of pure rubber and rubber bushings is described, which serves as a basis for correlating respective temperature dependencies.