Self-powered Asphalt-Based Sensors for Smart Roads

Abstract

Monitoring traffic conditions and pavement structures is essential for intelligent transportation systems. However, conventional sensors have limitations, including poor compatibility with pavement and high maintenance costs. Here, we present the concept of transforming asphalt from a pavement structural component to a sensing component and demonstrate its application in smart road systems. The functional asphalt was customized by adding piezoelectric materials into the asphalt matrix. We optimized its piezoelectric properties by improving the fabrication process and measured the electrical performance of the asphalt-based sensors. In the traffic monitoring experiment, we developed a system incorporating data acquisition, signal processing, and wireless transmission functions to capture tire-ground contact information. The details, such as speed and wheelbase, are decoded by a feature extraction algorithm and input into a support vector machine (SVM) classification model for training and testing. The model reaches a test accuracy of 97% in training a small-sample dataset. In addition, the self-powered asphalt-based sensor showed its feasibility and potential in the verification experiments of acoustic source localization (error = 2.4%) and energy harvesting. Our work proposes a low-cost, environmentally friendly, multifunctional material suitable for road sensors, potentially facilitating the large-scale implementation of smart road networks.