Intelligent Road Condition Monitoring using 3D In-Air SONAR Sensing

TL;DR

This paper explores the use of in-air 3D SONAR sensors for robust road surface monitoring, demonstrating high accuracy in material classification but moderate performance in damage detection, highlighting its potential for opportunistic sensing.

Contribution

It introduces a novel application of 3D SONAR sensors for road condition monitoring, showing robustness in harsh conditions and providing a new dataset for this purpose.

Findings

Road material classification achieved F1 scores near 90%.

Damage detection F1 score was around 75%.

SONAR sensing shows promise for pavement management systems.

Abstract

In this paper, we investigate the capabilities of in-air 3D SONAR sensors for the monitoring of road surface conditions. Concretely, we consider two applications: Road material classification and Road damage detection and classification. While such tasks can be performed with other sensor modalities, such as camera sensors and LiDAR sensors, these sensor modalities tend to fail in harsh sensing conditions, such as heavy rain, smoke or fog. By using a sensing modality that is robust to such interference, we enable the creation of opportunistic sensing applications, where vehicles performing other tasks (garbage collection, mail delivery, etc.) can also be used to monitor the condition of the road. For these tasks, we use a single dataset, in which different types of damages are annotated, with labels including the material of the road surface. In the material classification task, we differentiate between three different road materials: Asphalt, Concrete and Element roads. In the damage detection and classification task, we determine if there is damage, and what type of damage (independent of material type), without localizing the damage. We are succesful in determining the road surface type from SONAR sensor data, with F1 scores approaching 90% on the test set, but find that for the detection of damages performace lags, with F1 score around 75%. From this, we conclude that SONAR sensing is a promising modality to include in opportunistic sensing-based pavement management systems, but that further research is needed to reach the desired accuracy.