With increasing levels of driving automation, the information provided by automotive environment sensors becomes highly safety relevant. A correct assessment of the sensor’s reliability is therefore crucial for ensuring the safety of the customer functions. There are currently no standardized procedures or guidelines for demonstrating the reliability of the sensor information. Engineers are faced with setting up test procedures and estimating efforts. Statistical hypothesis tests are commonly employed in this context. 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. It also enables a more realistic representation of dependencies among errors. We show that different environmental conditions with an influence on sensor performance can be captured in the model through an inhomogeneous Poisson process and how statistical dependence among perception errors can be accounted for. Additionally we study how error correlation between several sensors impacts the perception reliability of an environmental model based on sensor fusion. To this end, we simplify the fusion problem with a majority voting scheme which implies that the multi-sensor system fails whenever more than half of the individual sensors commit unacceptable errors. With this interpretation the system reliability can be described by a k-out-of-n system. While the presented method does not encompass entirely the full complexity of the problem, it provides an initial systematic estimate of the necessary test effort and facilitates the use of sound statistical methods for test effort estimation.