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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.

Optimization of ride comfort is becoming increasingly important in chassis development. Constantly rising traffic density and comfort-orientated customer preferences are mainly responsible for this. Comfort and its improvement are important, not only on bad road surfaces, but also on even surfaces. The complexity of analysis leads to a strong link between car testing and simulation. The testing itself is divided in to roadtests and test stands. It is of outstanding importance to analyze the vehicles vibrational behavior from road tests as it is a real life situation. In order to get meaningful results from the roadtest the vehicle has to be seen as a complete vibrational system. The vibrational behavior of a system is clearly defined by input and output of the system. Road tests are chosen in relation to the predicted car. The roads surface is the input of the system exciting the vibrational subsystems of a car. The ride tests are used for the evaluation of drive response behavior.