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.

Authors: Pepe, Giovanni; Gabrielli, Leonardo; Ambrosini, Livio; Squartini, Stefano; Cattani, Luca
Affiliations: Universitá Politecnica delle Marche, Ancona, Italy; ASK Industries S.p.A., Montecavolo di Quattro Castella (RE), Italy(See document for exact affiliation information.)
AES Convention: 146 (March 2019) Paper Number: 10193
Publication Date: March 10, 2019 Import into BibTeX
Subject: Poster Session 3
Permalink: https://www.aes.org/e-lib/browse.cfm?elib=20326

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