  Knowledge about the maximum tire-road friction potential is an important
factor to ensure the driving stability and traffic safety of the vehicle. Many
authors proposed systems that either measure friction related parameters or
estimate the friction coefficient directly via a mathematical model. However
these systems can be negatively impacted by environmental factors or require a
sufficient level of excitation in the form of tire slip, which is often too low
under practical conditions. Therefore, this work investigates, if a more robust
estimation can be achieved by fusing the information of multiple systems using
a Bayesian network, which models the statistical relationship between the
sensors and the maximum friction coefficient. First, the Bayesian network is
evaluated over its entire domain to compare the inference process to all
possible road conditions. After that, the algorithm is applied to data from a
test vehicle to demonstrate the performance under real conditions.
