Assessment of road surfaces using sound analysis

Andrzej Czyzewski – andcz@multimed.org

Multimedia Systems, The Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, Gdansk, Pomorskie, 80-233, Poland

Jozef Kotus – Multimedia Systems, The Faculty of Electronics, Telecommunications and Informatics,
Grzegorz Szwoch – Multimedia Systems, The Faculty of Electronics, Telecommunications and Informatics],
Bozena Kostek – Audio Acoustics Lab., Gdansk Univ. of Technology, Gdansk, Poland

Popular version of 3pPAb1-Assessment of road surface state with acoustic vector sensor, presented at the 183rd ASA Meeting.

Have you ever listened to the sound of road vehicles passing by? Perhaps you’ve noticed that the sound differs depending on whether the road surface is dry or wet (for example, after the rain). This observation is the basis of the presented algorithm that assesses the road surface state using sound analysis.

Listen to the sound of a car moving on a dry road.
And this is the sound of a car on a wet road.

A wet road surface not only sounds different, but it also affects road safety for drivers and pedestrians. Knowing the state of the road (dry/wet), it is possible to notify the drivers about dangerous road conditions, for example, using signs displayed on the road.

There are various methods of assessing the road surface. For example, there are optical (laser) sensors, but they are expensive. Therefore, we have decided to develop an acoustic sensor that ‘listens” to the sound of vehicles moving along the road and determines whether the surface is dry or wet.

The task may seem simple, but we must remember that the sensor records the sound of road vehicles and other environmental sounds (people speaking, aircraft, animals, etc.). Therefore, instead of a single microphone, we use a special acoustic sensor built from six miniature digital microphones mounted on a small cube (10 mm side length). With this sensor, we can select sounds incoming from the road, ignoring sounds from other directions, and also detect the direction in which a vehicle moves.

Since the sound of road vehicles moving on a dry and wet surface differ, performing frequency analysis of the vehicle sounds is recommended.

The figures below present how the sound spectrum changes in time when a vehicle moves on a dry surface (left figure) and a wet surface (right figure). It is evident that in the case of a damp surface, the spectrum is expanded towards higher frequencies (the upper part of the plot) compared with the dry surface plot. Colors on the plot represent the direction of arrival of sound generated by vehicle passing by (the angle in degrees). You can observe how the vehicles moved in relation to the sensor.

Plots of the sound spectrum for cars moving on a dry road (left) and a wet road (right). Color denotes the sound source azimuth. In both cases, two vehicles moving in opposite directions were observed.

In our algorithm, we have developed a parameter that describes the amount of water on the road. The parameter value is low for a dry surface. However, as the road surface becomes increasingly wet during rainfall, the parameter value becomes more extensive.

The results obtained from our algorithm were verified by comparing them with data from a professional road surface sensor that measures the thickness of a water layer on the road using a laser beam (VAISALA Remote Road Surface State Sensor DSC111). The plot below shows the results from analyzing sounds recorded from 1200 road vehicles passing by the sensor, compared with data obtained from the reference sensor. The data were obtained from a continuous 6-hour observation period, starting from a dry surface, then observing rainfall until the road surface had dried.

A surface state measure calculated with the proposed algorithm and obtained from the reference device

As one can see, the results obtained from our algorithm are consistent with data from the professional device. Therefore, the results are promising, and the cheap sensor is easy to install at multiple points within a road network. Hence, it makes the proposed solution an attractive method of road condition assessment for intelligent road management systems.