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G. Pepe, L. Gabrielli, L. Ambrosini, S. Squartini, and L. Cattani, "Detecting Road Surface Wetness Using Microphones and Convolutional Neural Networks," Paper 10193, (2019 March.). doi: G. Pepe, L. Gabrielli, L. Ambrosini, S. Squartini, and L. Cattani, "Detecting Road Surface Wetness Using Microphones and Convolutional Neural Networks," Paper 10193, (2019 March.). doi:
Abstract: The automatic detection of road conditions in next-generation vehicles is an important task that is getting increasing interest from the research community. Its main applications concern driver safety, autonomous vehicles, and in-car audio equalization. These applications rely on sensors that must be deployed following a trade-off between installation and maintenance costs and effectiveness. In this paper we tackle road surface wetness classification using microphones and comparing convolutional neural networks (CNN) with bi-directional long-short term memory networks (BLSTM) following previous motivating works. We introduce a new dataset to assess the role of different tire types and discuss the deployment of the microphones. We find a solution that is immune to water and sufficiently robust to in-cabin interference and tire type changes. Classification results with the recorded dataset reach a 95% F-score and a 97% F-score using the CNN and BLSTM methods, respectively.

@article{pepe2019detecting,
author={pepe, giovanni and gabrielli, leonardo and ambrosini, livio and squartini, stefano and cattani, luca},
journal={journal of the audio engineering society},
title={detecting road surface wetness using microphones and convolutional neural networks},
year={2019},
volume={},
number={},
pages={},
doi={},
month={march},} @article{pepe2019detecting,
author={pepe, giovanni and gabrielli, leonardo and ambrosini, livio and squartini, stefano and cattani, luca},
journal={journal of the audio engineering society},
title={detecting road surface wetness using microphones and convolutional neural networks},
year={2019},
volume={},
number={},
pages={},
doi={},
month={march},
abstract={the automatic detection of road conditions in next-generation vehicles is an important task that is getting increasing interest from the research community. its main applications concern driver safety, autonomous vehicles, and in-car audio equalization. these applications rely on sensors that must be deployed following a trade-off between installation and maintenance costs and effectiveness. in this paper we tackle road surface wetness classification using microphones and comparing convolutional neural networks (cnn) with bi-directional long-short term memory networks (blstm) following previous motivating works. we introduce a new dataset to assess the role of different tire types and discuss the deployment of the microphones. we find a solution that is immune to water and sufficiently robust to in-cabin interference and tire type changes. classification results with the recorded dataset reach a 95% f-score and a 97% f-score using the cnn and blstm methods, respectively.},}

TY - paper
TI - Detecting Road Surface Wetness Using Microphones and Convolutional Neural Networks
SP - EP -
AU - Pepe, Giovanni
AU - Gabrielli, Leonardo
AU - Ambrosini, Livio
AU - Squartini, Stefano
AU - Cattani, Luca
PY - 2019
JO - Journal of the Audio Engineering Society
IS -
VO -
VL -
Y1 - March 2019 TY - paper
TI - Detecting Road Surface Wetness Using Microphones and Convolutional Neural Networks
SP - EP -
AU - Pepe, Giovanni
AU - Gabrielli, Leonardo
AU - Ambrosini, Livio
AU - Squartini, Stefano
AU - Cattani, Luca
PY - 2019
JO - Journal of the Audio Engineering Society
IS -
VO -
VL -
Y1 - March 2019
AB - The automatic detection of road conditions in next-generation vehicles is an important task that is getting increasing interest from the research community. Its main applications concern driver safety, autonomous vehicles, and in-car audio equalization. These applications rely on sensors that must be deployed following a trade-off between installation and maintenance costs and effectiveness. In this paper we tackle road surface wetness classification using microphones and comparing convolutional neural networks (CNN) with bi-directional long-short term memory networks (BLSTM) following previous motivating works. We introduce a new dataset to assess the role of different tire types and discuss the deployment of the microphones. We find a solution that is immune to water and sufficiently robust to in-cabin interference and tire type changes. Classification results with the recorded dataset reach a 95% F-score and a 97% F-score using the CNN and BLSTM methods, respectively.