| Summary: | The classification of road surface wetness is important for both the development of future driverless vehicles and the development of existing vehicle active
safety systems. Wetness on the road surface has an impact on road safety and is one
of the leading causes of weather-related accidents. Although machine learning algorithms such as recurrent neural networks (RNN), support vector machines (SVM),
artificial neural networks (ANN) and convolutional neural networks (CNN) have
been studied for road surface wetness classification, the improvement of classification performances are still widely being investigated whilst keeping network and
computational complexity low. In this paper, we propose new CNN architectures
towards further improving classification results of road surface wetness detection
from acoustic signals. Two CNN architectures with differing layouts for its dropout
layers and max-pooling layers have been investigated. The positions and the number
of the max-pooling layers were varied. To avoid overfitting, we used a 50% dropout
layers before the final dense layers with both architectures. The acoustic signals of
tyre to road interaction were recorded via mounted microphones on two distinct
cars in an urban environment. Mel-frequency cepstral coefficients (MFCCs) features
were extracted from the recordings as inputs to the models. Experimentation and
comparative performance evaluations against several neural networks architectures
were performed. Recorded acoustic signals were segmented into equal frames and
thirteen MFCCs were extracted for each frame to train the CNNs. Results show that
the proposed CMCMDD1 architecture achieved the highest accuracy of 96.36% with
the shortest prediction time.
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