The road condition is an important factor for driving comfort and has impact on safety, economy and health. Delayed detection of defects lead to renewals which yields to complete roadblocks or traffic jams. Therefore, an early identification of road defects is desirable. Novel condition monitoring systems employ vehicles as sensor platforms and apply machine learning methods to predict the road condition based on the sensor data. The paper addresses the question how to combine the classification results from different vehicles to improve the final prediction. Different fusion strategies are investigated in various scenarios in a novel simulation. It is demonstrated that the performance of the classification can be improved compared to a majority vote or only considering one vehicle by taking the probability for the prediction of each vehicle into account. The probabilities follow a multinomial distribution and the precision matrix of the classifiers provide the best parameters. Overall, the results show that the application of the presented fusion strategies on road condition estimation greatly improve the performance and guarantee a robust detection of defects.