Abstract

Structural Health Monitoring (SHM) is essential to assess the accuracy of durability predictions of structures. Using low-cost sensors on structural evaluation has gained significant attention compare to high-cost sensors. Although these may not be as accurate and sensitive as the expensive electronic devices, with efficient code and right use, there is a possibility of getting useful information from them. These sensors can vary based on their functionality and the measurements they provide. For example, one is highly sensitive to the light of its environment while the other kind would give different results in different temperatures. In this paper, three different displacement measuring sensors have been studied. An ultrasonic sensor (HC-SR04) and two different types of laser sensors (VL53L0X and VL53L1X) are investigated in the paper. An Arduino Mega has captured their measured data, and a raspberry pi has made the acquisition. Not only issues regarding coding and placing of these sensors have been presented ultimately, but precise solutions for the aforementioned problems as well as an efficient way of assembling all the sensors are also presented in this paper. The data generated from these electronic devices can be used for Structural Health Monitoring applications.

Abstract

The introduction of light emitting diodes (LED) in automotive exterior lighting systems provides opportunities to develop viable alternatives to conventional communication and sensing technologies. Most of the advanced driver-assist and autonomous vehicle technologies are based on Radio Detection and Ranging (RADAR) or Light Detection and Ranging (LiDAR) systems that use radio frequency or laser signals, respectively. While reliable and real-time information on vehicle speeds is critical for traffic operations management and autonomous vehicles safety, RADAR or LiDAR systems have some deficiencies especially in curved road scenarios where the incidence angle is rapidly varying. In this paper, we propose a novel speed estimation system so-called the Visible Light Detection and Ranging (ViLDAR) that builds upon sensing visible light variation of the vehicle's headlamp. We determine the accuracy of the proposed speed estimator in straight and curved road scenarios. We further present how the algorithm design parameters and the channel noise level affect the speed estimation accuracy. For wide incidence angles, the simulation results show that the ViLDAR outperforms RADAR/LiDAR systems in both straight and curved road scenarios. Publisher's Version