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 maintainance costs and effectiveness. In this article 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.
Detecting road surface wetness using microphones and convolutional neural networks / Pepe, G.; Gabrielli, L.; Ambrosini, L.; Squartini, S.; Cattani, L.. - ELETTRONICO. - (2019). (Intervento presentato al convegno 146th Audio Engineering Society International Convention: Excite Your Ears, AES 2019 tenutosi a The Convention Centre Dublin, irl nel 2019).
Detecting road surface wetness using microphones and convolutional neural networks
Pepe G.;Gabrielli L.;Ambrosini L.;Squartini S.;
2019-01-01
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 maintainance costs and effectiveness. In this article 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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
