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SMART OPTICAL SENSOR FOR ADAPTIVE, PREDICTIVE, AND ON-DEMAND CONTROL OF PUBLIC LIGHTING

  • US 20160050397A1
  • Filed: 05/08/2014
  • Published: 02/18/2016
  • Est. Priority Date: 05/08/2013
  • Status: Active Grant
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First Claim
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1. A smart apparatus for adaptive, predictive, and on-demand control of public lighting, characterized in that it comprises at least one local device, equipped with optical means for acquisition of images and/or video streams, data-processing means, as well as further means for:

  • continuous and automatic on-board analysis of the set of variables regarding at least traffic conditions, weather conditions, and degree of luminance of the stretch of road being analysed;

    real-time determination of the effective operating lighting category of the stretch of road being controlled;

    adaptive control of the public-lighting system, through determination of the optimal profiles of activation of the lighting itself, according to the aforesaid operating lighting category; and

    on-demand control of lighting through real-time variation of the light produced by each individual lighting point controlled by the local device itself;

    wherein said further means comprise;

    at least one first software module for acquisition and processing of the data acquired and extrapolation of the set of variables regarding the traffic conditions mentioned in the previous point, which will offer at least the following functions;

    acquisition of images via videosensor;

    automatic learning of a historic model of the background of the scene acquired;

    segmentation of the moving objects present on the scene;

    counting of the moving objects present on the stretch of road being controlled;

    measurement of their instantaneous and average speed;

    classification of the type of moving objects (pedestrians, two-wheeled vehicles, motor vehicles, heavy vehicles);

    analysis of the origin and destination of the moving objects;

    tracking of the moving objects;

    determination of the direction of entry and exit of each moving object, which in turn enables processing of the matrix of the flow of vehicles and/or pedestrians for the stretch of road analysed; and

    counting of parked vehicles;

    at least one second software module for acquisition and processing of the data, and extrapolation of the set of the aforesaid variables regarding the weather, affording at least the following functions;

    acquisition of images via videosensor;

    recognition of the presence of fog and consequent removal thereof from the image acquired;

    computation of the maximum distance of visibility as a function of the amount of fog present;

    recognition of precipitations, such as rain, hail, snow, for determining the condition of the surface of the stretch of road, such as wet or dry;

    analysis of the condition of cloud on the scene for estimation of the visual task;

    at least one third software module for acquisition and processing of the data and extrapolation of the set of the aforesaid variables regarding the degree of luminance and brightness, affording at least the following functions;

    acquisition of images via videosensor;

    calibration of the sensor itself in order to correlate the intensity of the pixels of the image acquired with the corresponding luminance on the scene, after prior regression of a function that correlates the two variables considered;

    measurement of the luminance of the scene via a procedure that can be applied indifferently to any digital videosensor, such as for example;

    CMOS, CCD, with HDR, etc., which uses the function obtained in the calibration stage, the current settings of exposure of the sensor and the values of brightness of the pixels in the current image to derive the luminance of each point corresponding to the pixel on the scene;

    automatic learning of a historic model regarding the brightness of the scene;

    indirect measurement of the degree of brightness of the scene with respect to the historic model; and

    measurement of the colour-rendering index (CYI);

    at least one fourth software module, which, starting from the aforesaid variables extracted via said first, second, and third modules, is able to learn automatically the historic evolution of the aforesaid variables with methods of automatic learning, and predict the future evolution thereof, accordingly determining the operating lighting category provided for the stretch of road being controlled;

    said module regulating the luminous flux of the corresponding lighting system being managed, in an adaptive way with respect to the effective traffic conditions, and in compliance with the minima of luminance required for the aforesaid category; and

    at least one fifth software module, which, starting from the aforesaid variables regarding at least traffic conditions, weather conditions, and degree of luminance of the stretch of road analysed, is designed for real-time recognition of the occurrence of significant events and instantaneous prediction of their future evolution, reacting accordingly via real-time re-modulation of the luminous fluxes of the corresponding public-lighting system, with the aim of guaranteeing the minimum degree of luminance required by the standards, favouring energy saving, and increasing the safety of the stretch of road.

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