摘要：AbstractModern vehicles are equipped with numerous sensors and hence offer an increasing degree of environmental perception. In this work, a method is presented that is able to classify different road types and their conditions based on standard vehicle sensors. Therefore, training and validation data on two routes in urban traffic and on federal highways was gathered using a Volkswagen Golf GTE Plug-In Hybrid. The method uses features based on both frequency and time domain extended with a physical vehicle sub-model. For the classification a decision tree model is trained offline and implemented for online use on target hardware commonly used in modern vehicles. A Bayesian and Markov based filter is used to smooth the output and increase the accuracy of the classification.Since the method is based on sensors that are available in modern vehicles, there is no need for additional hardware, reducing the effort required for implementation. Results show promising classification performance, especially for classifying cobblestone. The three classes of good, medium and bad asphalt labeled relatively precise despite very similar characteristics. Possible applications of the approach could be to adapt vehicles suspension and driving dynamics, to parameterize driver assistance systems, or to update road maps according to their current condition.
