Introduction of intelligent vehicle detection sensors

Abstract

The trafFc control system of the MetropolitanExpressway is collecting and processing traffic data obtained from vehicle detection sensors installed along the highway. This traffic data is then offered to the users of the highway as information on required travel time, congestion and the like.Most of the sensors installed along the Metropolitan Expressway are ultrasonic sensors. However, there have been several problems associated with previous types of ultrasonic vehicle detection sensors: 1.) in tunnels and on three lane sections of the highways, previous types of sensors detected vehicles erroneously due to the reception of unnecessary reflected signals which caused a decrease in the accuracy of detection, 2.) due to the fact that there are more than about 2,000 sensors installed, and 3.) speed measurement.This report is an outline of the intelligent vehicle detection sensors (hereafrer, I Type vehicle detection sensors) introduced to solve these issues.

Density-Based Statistical Clustering: Enabling Sidefire Ultrasonic Traffic Sensing in Smart Cities

“…In the field of radar and ultrasonic sensors, several top-down sensor solutions are available, which require the placement above a specific street lane for single-lane car profile measurements [10,[14][15][16][17][18]. These techniques allow obtaining the complete height profile of vehicles for detection and classification but require a gantry or bridge over the street for mounting the sensors, which is infeasible in many situations.…”

“…For the ultrasonic sensors, the processing is strongly simplified, as with the available systems, only the distance to the first relevant reflection and not the complete impulse response is evaluated. The significantly lower propagation speed and resulting decay time of the impulse responses together with the requirements for a sufficient pulse repetition rate (typically ≥10 Hz) of ultrasonic sensors can lead to self-interference and also to issues with fast-moving traffic participants [18]. For ultrasonic sensors, previous studies have shown that the impact of weather effects such as wind, snow, and rain is only marginal [19,20].…”

Research on the Detection and Tracking of Moving Target based on Kernel Method

“…The mathematical morphology has a complete mathematical basis, which used to analysis and processing for morphological [15][16][17]. The inter frame difference method using two frames of adjacent image difference operation to get the moving object, the algorithm is simple and easy to realize, the adaptability is strong, but when the moving target due to the excessively high background exposure between adjacent frames is large, easy to expose the background detection for moving object detection, which leads to target the ghosting phenomenon.…”

Model of Image Color Difference and Partial Based On RGB Color Distribution Measuring

“…Artificial color detection rely on visual observation identification, observation results are influenced by the individual differences and experience and other factors, can only do a rough estimate of the chromatic aberration is unable to obtain quantitative results, the partial color judgment is entirely dependent on the observer's experience, so the color accuracy is not high, mainly used in the dyeing production site; color measurement instrument is measured by measuring the color of fabric surface on the visible light reflectance (380nm~780nm), the XYZ value in getting all the reflectance spectra of color three stimulation, according to the obtained the measured color difference fabrics and fabric sample fabric and partial color difference formula and hue formula [2]. Color measurement results are accurate color measurement instrument, but only for a small range (color measurement instrument window) in color measurement, color measurement can not realize the large area, also asked the fabric smooth surface drying [3].…”