## 4 Vibration-Based Road Friction Monitoring
### High Frequency Vibrations
The experimental results showed a correlation between the frictions measured using the British pendulum and the skid trailer, where the coefficient of determination was \(R^{2}=0.69\). Concerning the correlation coefficient between the frictions measured using the skid trailer and the surface texture, no correlation was found at low spatial frequencies. However, the correlation increased significantly for spatial frequencies greater than 30 rad/mm. Therefore, the results indicated that the shortest measured wavelengths, about 130 \(\upmu\)m and below, contributed the most to the differences in wet rubber friction. Last but not least, the authors highlighted the influence of temperature, as the correlation was significantly reduced when data captured at different temperatures was considered. In this context, [PERSON] [46; 131] presented an acoustic-based approach for automatically classifying the road friction coefficient. The acoustic data were collected using a cardioid microphone. The microphone was installed on the suspension strut of a small electric vehicle, taking advantage of the fact that Electric Vehicles (EV) emit less noise compared to Internal Combustion Engine (ICE) ones. Foam rubber shield was used to suppress wind noise. Data were collected at low constant vehicle speeds 2.77-8.33 m/s (10-30 km/h). In brief, the employed system processed audio signals and extracted features such as linear predictive coefficients (LPC), mel-frequency cepstrum coefficients (MFCC) and power spectrum coefficients (PSC). The features were extracted using time windows of 0.02, 0.05 and 0.1 s to facilitate real-time application. The most informative features were then selected with the aim to train an Artificial Neural Network for the road friction potential classification. Authors evidenced that the method was able to distinguish between asphalt, gravel, snowed and stone roads at 91% accuracy in the worst case. Although it was not mentioned exactly how the road friction coefficient was inferred, this was probably conservatively estimated based on generic values for the different road types. [PERSON] et al. [49] also detected wet road surface conditions by analysing tyre-road noise in real-time. Field trials were conducted with an instrumented passenger vehicle, on the chassis of which an electret microphone was installed, at the rear right wheel. An anti-wind foam was attached to the microphone to minimize wind and turbulence effects, and the tyre size was 195/65 R15. The road surface in the field trials was a dense asphalt concrete mixture with maximum 12 mm chippings, in low-to-medium worn condition. The vehicle speed in the tests ranged between 1.38-19.44 m/s (5-70 km/h), however the validity of the method was proved for speeds higher than 8.33 m/s (30 km/h). The measurements were first performed on dry and then on wet asphalt. The tyres were warmed before performing the tests. During the tests performed on wet asphalt, the test section was constantly watered. The water thickness was not measured, however the track was considered wet only when there was water sprayed and splashed by the passing test vehicle. The audio samples were processed in chunks by a 1/3 octave filter band, in the frequency range from 20 Hz to 20 kHz. Two different methods were applied for selecting the most informative features, which were then used to train a Support Vector Machine (SVM) for the classification problem, following the approach depicted schematically in Figure 23. The experimental results showed that when the vehicle was driven on the wet asphalt, the noise level increased. The level increase was noticeably higher in the 500 Hz to 8 kHz range. The proposed classifier had no misses for the wet asphalt status detection, and less than 12% misses for the wet to dry transition. Average response time of the system for the dry to wet transition was very low, in the order of 0.2 s, while for the wet to dry transition it was longer, in the order of several seconds.