Automotive surface identification system

In this paper the practical issues of automotive surface identification system development are considering. The novelty of this work is the combining of different training algorithms, neural network structures and methods to increase the classification accuracy and avoid overfitting of real-world data. The obtained results thereby demonstrate that the use of proposed system architecture and statistical methods allow for reliable discrimination of various road surfaces in real driving condition.