The novel DMT-sensor system depends on the V-Q-x induction mechanism realizing the self-powered intelligent perception in traffic IoT scenario

Abstract

Focusing on the rapid development of traffic IoT (Internet of Things), the application and interaction of modern transducer in real transportation system has been more challenged. To satisfy its comprehensiveness, convenience and adaptability, this paper proposes a brand new sensor (DMT-sensor) based on triboelectric technology for intelligent road perception. Different from the general piezo or magneto-electricity, the sensor is mainly composed of the strongly electronegative triboelectric layer and induction electrode. It can output pulse-like voltage signals with larger amplitude and wider width when the tribo-surface is contacted. In experiment section, the DMT-sensor not only completes the judgement on the motional direction, but also can provide clear feedback for different human actions, towing and mixed motion scenes. According to the V-Q-x theoretical model of TENG, there is a strong correspondence between the relative displacement x of tribo-layers and the induced voltage V, which makes it possible for the sensing module to measure object’s velocity. The C1 and C2 units of DMT-sensor have a speed measurement accuracy more than 90 % for a smart car controlled by the close-loop system (v_r: 0.68–3.40 m/s). It is still able to respond to subtle movements such as turning and climbing on the feedback signal. Furthermore, the DMT-sensor maintains reliable adaptability in harsh road conditions such as sand, rain, and mud road. In addition, this paper completed the simple traffic monitoring system by LabVIEW and high-speed acquisition instrument, which further exhibited its practical application potential. As a self-powered sensor device, DMT-sensor has the characteristics of high performance, convenience and low running cost. Compared with the general sensing technology, this is undoubtedly a more novel and positive solution for intelligent sensing system in traffic scenario.