The Stack The problem has been approached before with the creation of an asphalt status classification system powered by a Support Vector Machine (SVM), but the range of surface types practicable within the system was limited and the results hampered by false predictions caused by unrelated input such as pebbles. by Martin Anderson
'The researchers of the new work, led by Irman Abdić at the Institute of Electrical and Electronics Engineers (IEEE), used Recurrent Neural Networks to monitor audio input from tyre-road contact in real-world conditions and at varying speeds around the Greater Boston area, using an inexpensive shotgun microphone affixed near the rear tyre of a 2014 Mercedes CLA. The results achieved in these initial tests reached an unweighted average recall (UAR) of 93%, a notable success rate. Since the audio monitoring continues even when the car is at rest, wetness identification can still be achieved when the vehicle is at a standstill, though this is via the presence of other passing vehicles and the sounds that they are making.
'This study, along with so much current machine learning research, seems to rely on the annotation and classification of existing infrastructure, rather than the development of geo-neutral filters and algorithms which could function discretely in undocumented environments. The performance of the vehicle and apparatus in the roads which the tests were conducted on was measured with the International Roughness Index, a standard of surface quality.
'This study is the first time that Long short-term memory RNNs (LSTM-RNNs) have been used to attack this problem. LSTM-RNNs have been used extensively in audio-based work, including for the identification of phonemes, animal species identification and individuation of temporal structure in music.'
arxiv PDF here