Patent Description:
One of the most significant documents on the energy efficiency of lights in patent literature is <CIT>.

This document proposes to modify the parameters of public lighting through current or expected weather data.

This system is interesting but it does not take into consideration the current methods and design legislation of lighting systems and does not appear to be of real assistance in actual energy saving, which should likewise be based on the intensity of use of the area.

Current lighting technology design criteria provide a first sizing of the lighting based on the geometry of the areas to be lit, whereby measuring parameters are taken into consideration, such as the length or width of the roads, etc..

Sizing proceeds by making changes based on risk variables, such as for example, the presence of crossroads, corners, etc..

Finally, it is desirable for the design to take into consideration actual traffic variables. However, the data relative to the latter are rarely available.

In fact, a cautionary ratio is applied that tends to light more than what is really required.

Added to this is the fact that in many countries there is road lighting technology classification legislation, and the system is to illuminate based on the appurtenant lighting technology classes. The classes are assigned in a cautionary manner, whereby they often provide greater lighting than necessary. In general, class lowering rules are provided based on the actual traffic conditions, but given that the data thereon are difficult to obtain and often lacking, the waste of energy on roads to date remains at an unacceptable level.

A further document in the field is <CIT>, which describes a system for controlling the intensity of public lighting based on the perception and processing of images. <CIT> aims at a minimal communication system between lighting points. In particular, the lighting points change light intensity when a movement is perceived in the visual field or a change in intensity of a nearby street lamp is perceived. In this way, the street lamps communicate with the ones nearby without the need for a complex system.

This system allows keeping the lighting at a low level when it is not required, but it does not allow lowering the lighting technology classes initially assigned to the project because it does not allow having traffic data.

Said system also has other disadvantages, in particular the cameras are costly, they have a limited sensing radius whereby there are to be several of them, and they can be tricked if there are shields in the visual field such as, for example trees that change their foliage seasonally or road signage. Moreover, the visual system described is effective only for sensing "movement", but it is not capable of effectively sensing a stopping, for example for a conversation between people, which equally requires lighting.

<CIT> also suggests an alternative system in which the lighting is controlled through a sound sensing rather than through a visual sensing. In this regard, the street lamps are provided with microphones and the system has a control and communication unit that establishes which street lamp is closest to the sound source, and it increases the intensity thereof. Therefore, this solution is more complex than the preceding one, but it also does not provide traffic data and therefore the possibility of prediction is lacking, thus not allowing the lighting class initially assigned to the project to be lowered.

Finally, <CIT> suggests a hybrid between the visual and sound solution in which the street lamps initially are turned OFF, and where the sound sensing is only used to turn them ON and make the visual system operational. Then, the decision is made to adjust or turn OFF the lighting unit, if other nearby sources already provide the scene with enough light.

A more complex system, in particular both adaptive and predictive, is described in <CIT>.

This system is adaptive because it aims to declassify lighting technology, and is predictive because is adjusts the light flow based on instant predictions of events. To achieve these goals, the system of <CIT> makes use of a visual perception artificial intelligence, i.e. taught to learn and classify traffic data based on images.

In general, this system is very costly due to the main component required, that is the cameras. Moreover, its instant prediction ability and reactivity based on images alone are limited because while the cameras are arranged in an appropriate manner, the vision of the action field might not be optimal due to obstacles or orientations that provide overlapping of the vehicles. Moreover as is known, images are projections of light that bounces off objects and travels in a straight line alone, thereby it is not possible to perceive images from behind a crossroad or traffic sign or tree with one camera alone, therefore it is not possible to predict, for example the turns of the vehicles. To obviate this drawback, the number of cameras should be high, but this obviously would increase the costs.

A hybridization with the systems described by <CIT> would simply result in an artificial intelligence with learning based totally on the images, as in <CIT> where sound is only used to turn ON the scene and allow the viewing thereof.

The object of the present invention is to overcome all or some of the drawbacks of the known technique.

In particular, it is the general object of the present invention to increase the energy saving in the field of lighting.

It is a further general object of the present invention to provide a process and a lighting system that is predictive and adaptive, i.e. capable of self-learning traffic data and using them:.

all in alternative manner with respect to the known predictive and adaptive systems.

It is another further object of the present invention to improve the instant prediction and the reactivity with respect to the known systems with visual artificial intelligence.

It is another further object of the present invention to lower the cost of the predictive and adaptive system with visual artificial intelligence.

It is another further object of the present invention to make possible the light adaption in forecast of turns of a vehicle or in case of stopping.

Advantageously, with respect to a visual perception artificial intelligence, after the training, the artificial intelligence based on sound of the present invention allows sensing operations of the visual field at a greater distance or in visually prohibited areas. Moreover, such method has a greater predictivity because it can distinguish various situations associated with a same image, thus creating different sample soundtracks for a same image, which are distinct from the sound diversity, such as a straight continuation or a turn. The method is also capable of collecting increased data with respect to a visual artificial intelligence because it can proceed with a processing by comparison and association of detected sound data following the training to obtain therefrom the learning of situations which can escape an image sensing.

Advantageously, the artificial intelligence based on sound can be used to correlate the vehicle traffic data with the levels of sound pollution and/or with the wear of the road infrastructures.

Another further advantage is the one of making possible the automatic signalling of anomalous events within the road context, such as by way of non-limiting example, automobile collisions.

Preferably, the visual and sound sensing zone is smaller than said area and can be modified, for example moved within said area.

Advantageously, the overall cost of the system is particularly low because it takes advantage of a low number of image sensing means, which for example are cameras, and an increased number of sound sensing means, which for example are microphones, where the cost of the second is much lower than the cost of the first.

"Zones which overall are smaller than said area" means that the overall visual field of the image sensing means covers a smaller area than the whole lit area, or additionally or alternatively, that there are fewer image sensing means than there are sound sensing means.

Preferably, there are various sample zones during the step of sound training by means of images. In this way, the number of image sensing means required is further reduced, it being possible to take advantage of the same means in different zones to increase the sample record of cases.

Further characteristics and advantages of the present invention will become clearer from the following detailed description of the preferred embodiments thereof, with reference to the appended drawings and provided by way of indicative and non-limiting example. In such drawings:.

For the purposes of the present invention, we conventionally consider the term "traffic" as a generic word that comprises the passing by or stopping of any object, animate such as a living being, or inanimate such as a vehicle <NUM>, that requires subsequent lighting. Therefore, "traffic" is intended both outdoor transit, such as for example on a road, and indoor transit, such as in household environments. The lit area involved by the system of the present invention is therefore both public and private, both outdoors and indoors.

With reference to <FIG>, it shows an example and/or case of use of a lighting system according to the present invention, indicated as a whole with reference number (<NUM>).

The system (<NUM>) comprises a monitored area (<NUM>) in which there are for example, various roads (<NUM>) and (<NUM>).

The system (<NUM>) comprises a plurality of lighting devices (<NUM>), for example street lights, distributed in the area, generally along the roads, each controlled by means of control means (<NUM>) to vary the lighting settings, they for example, being adjustable. Each control means (<NUM>) can be provided with any data network topology, for example wireless networks LPWAN, WIFI, <NUM>, <NUM> or equivalent cabled networks.

The system also comprises a artificial intelligence (<NUM>) based on sound for learning traffic data and the relative determination of the lighting and/or lighting technology classification settings.

The artificial intelligence comprises a plurality of intelligent devices (<NUM>) arranged to cover the monitored area (<NUM>), where each intelligent device comprises a local neural network (21a) for learning traffic data based on sound and for processing local lighting commands; the intelligent devices further comprise sound detecting means interacting with the local neural network, for example one or more microphones (<NUM>).

Preferably, the intelligent devices (<NUM>) are coupled to the lighting devices, there for example, being one for each lighting device (<NUM>). However, embodiments that are less costly but in any case effective are possible, where only a portion of the lighting devices (<NUM>) is coupled to intelligent devices (<NUM>), therefore provided with microphones (<NUM>) in communication with at least one local neural network (21a). In this last case, the remaining lighting devices (<NUM>) are preferably coupled to control means (<NUM>) to vary the lighting settings, they being for example, adjustable according to the indications of at least one nearby intelligent device (<NUM>).

The microphones are for example, of the MEMS type or similar, preferably integrated in the lighting device (<NUM>).

The artificial intelligence further comprises:.

The artificial intelligence also comprises:.

In particular, each general neural network (21b) allows processing traffic data, where the data processed come from the intelligent devices (<NUM>). It decides for example, the lighting technology classes, or more generally, the lighting patterns in which the intelligent devices can decide the local lighting settings, and transmits them to the intelligent devices. The general neural network also receives training information for recognizing traffic data and transmits them to the intelligent devices (<NUM>), as clarified below.

Through the local processing means, the intelligent devices decide local lighting settings within constraints set by said information received from the general neural network.

The communication for example, takes advantage of the data network topology, for example wireless networks LPWAN, WIFI, <NUM>, <NUM> or equivalent cabled networks, preferably the one of the control means of the lighting devices, so for example, the intelligent devices can be integrated therein.

The artificial intelligence also comprises training means (<NUM>), for example in kits that preferably can be at least partially associated with and disassociated from the system (<NUM>), so as to have a temporary use.

In particular, the training means comprise a neural training network (21c), sound detection means and image detection means (<NUM>) that cooperate with one another.

The sound detection means can for example, be microphones (<NUM>) permanently present in the system (<NUM>), such as the microphones of the intelligent devices (<NUM>), or they can be microphones belonging only to the training kit and therefore removable from the system and movable in different points.

The image sensing means (<NUM>) comprise for example, one or more cameras. They preferably are movable in different points of the system and can be associated with and disassociated from it. They belong solely to the training kit and are preferably fewer in number than the microphones of the system.

As is clarified below, the image sensing means serve to teach the artificial intelligence to recognize and classify the traffic sounds, whereby it is sufficient to install them only in one or more points representative of the monitored area, or move them between the points. At the end of the teaching process, it is possible to also provide the removal thereof from the system (<NUM>). The image sensing means in the present invention therefore do not require to be spread throughout the whole network or to perpetually be a part thereof.

The artificial intelligence (<NUM>) based on sound acquires sound data and translates them into traffic data in the following manner.

The monitored area is divided into zones, for example each corresponding to a road or square, initially catalogued in a memory of the lighting system with a base appurtenant lighting category that establishes the initial lighting settings implemented; in particular it is preferable for the settings to establish schedules, duration and dimming magnitude applicable, which is intended as a reduction of light flow. Such initial lighting class assigned is also called "project class".

In an initial learning step, it is possible to teach the artificial intelligence (<NUM>) to recognize the type of vehicle and/or traffic events from the sound spectrogram. This can be done by sensing images of the vehicles in transit, preferably only in one or some sample zones (<NUM>) of the area of the system, by means of the visual sensing means. Different visual recognition software is present on the market that is capable of processing the images to recognize what they depict and to classify them based on their object, for example by associating them with a vehicle, which vehicle, or other. For the purposes of the present invention, any visual recognition software and automatic classification of images can be used, including one of those already on the market. Such classified images are associated with the sound spectrum registered simultaneously with the images, by transferring thereto the classification thereof and thus creating sample soundtracks (<NUM>).

Determining the sample soundtracks can occur for example, as shown in <FIG>.

The training means (<NUM>) simultaneously capture a video (<NUM>) and a spectrogram (<NUM>) relative to the scene shot in the video.

The spectrogram in <FIG> shows the frequency composition of sound expressed in Hz, indicated to time expressed in seconds.

The training means then isolate the parts of the spectrogram in which the frequency is greater than a predetermined threshold (<NUM>), called silence threshold, above which the system understands that something is happening. The training means then associate the portions of spectrogram with corresponding portions of video. The portions of video are recognized and classified based on the visual recognition software and the training means transfer such classification to the corresponding portion of spectrogram, obtaining sample soundtracks (<NUM>), that is classified portions of spectrogram.

It is worth noting that the visual and sound recording frequency is different, whereby each frame of the video shot corresponds to a range of the soundtrack.

The system can also understand which vehicles are transiting, at what frequency, etc., whereby it is then possible to also generate statistical traffic data.

Once the system has been taught to classify sound by associating classified images sensed only in predetermined zones of the system, it is possible to move to a step of acquiring a data history using only detected sound data in the whole area of the neural network. The visual sensing means (<NUM>) can for example, be removed from the system and used in another similar, newly-installed system in a different area. They can in fact form a training kit that can be associated with and disassociated from the system.

In the step of acquiring a data history, the training data, that is the sample soundtracks, are transferred to the intelligent devices which therefore are autonomously capable of recognizing the events based on the sound, and of transferring the relative data to the general neural network (21b).

Once a first history of the traffic data detected by means of sound taught by means of classified images is established, the general neural network (21b) processes them and assigns a corrective to each road with respect to the design lighting category, thus establishing a true zone mapping of the actual lighting needs as a function of traffic. The artificial intelligence then sends the lighting parameters to be implemented based on the new classes, such as for example the dimming and/or tone profiles, to the intelligent devices.

Thanks to the training data (<NUM>) received, the intelligent devices (<NUM>) are also capable of autonomously making an instant behaviour prediction of the traffic and of adapting the performance of the lighting devices (<NUM>) within the lighting settings received from the artificial intelligence, in particular of the lighting class assigned to their zone, as a function of the actual needs.

The system is more efficient with respect to a system having visual perception alone because unlike light, sound is capable of overcoming obstacles; we can in fact perceive noise from behind a corner even if we cannot see what is causing it. Moreover, different noises can be associated with a same movement. Imagine for example, a vehicle nearing a crossroad: the engine noise is different if it intends continuing straight on or turning. This generates a discernment that is not possible for a visual perception artificial intelligence alone and results in general in a prediction provided in advance with respect to the continuation of the action of the vehicle that accordingly allows controlling the lighting means that are on the path, including those around a corner.

The system of the present invention is also capable of perceiving stopping noises, such as a running engine of a stopped vehicle, or the conversation between two people. Here, the system controls an appropriate lighting, something that a system based on a visual perception artificial intelligence alone could not do.

The system is also capable of being progressively refined because the neural network (21b) can continue acquiring and processing a data history in a continuous manner, and accordingly correct the lighting settings.

Also the training step can be progressive, generating training updates progressively transmitted to the intelligent devices (<NUM>).

The present invention is adapted to control any lighting variation, including the intensity and colour tone. For example, when people are sensed through steps or a voice, it could be desirable to change the tone, switching for example, from cold to warm light.

<FIG> shows an example in which the main road (<NUM>) intersects the closed road (<NUM>).

The two roads could have a same initial lighting category, then the closed road (<NUM>) is reclassified based on the traffic detected sound data to a lower class, giving lighting priority to the main road (<NUM>).

From a practical operational viewpoint, the noise generated by the vehicles originates from different components, all recognizable by the system, in particular:.

The noise generated by the engine of motor vehicles at low speeds is greater than the noise generated by the rolling of the tyres on the asphalt. As the speed increases, the rolling noise increases in intensity up to prevailing over the noise generated by the engine.

Therefore, a measurement centred about sound contributions due to the rolling component of the sound allows the system to also deal with the increasing expansion on the market of electric vehicles and to catalogue roads intended for any speed.

Concerning the spectrum of frequencies of the sound waves generated by a lightweight vehicle and by a heavy vehicle, taken for example respectively in <FIG>, it is worth noting how it is possible to discriminate the different types of noise source (engine A, rolling B, aerodynamics, etc.) based on the frequencies. It is worth noting that it is possible to filter the transduction of the signal into a specific frequency band, for example from <NUM> to <NUM>, thus processing only the sound contribution due to the rolling of the tyres.

This is useful for example, for counting the number of vehicles in transit or for establishing the periods of greater traffic congestion, etc..

In understanding the object of the present invention, the term "comprising" and its derivatives, as used herein, are intended as open-ended terms that specify the presence of declared characteristics, elements, components, groups, integers and/or steps, but do not exclude the presence of other undeclared characteristics, elements, components, groups, integers and/or steps. The above also applies to words that have similar meanings such as the terms "comprised", "have" and their derivatives. Furthermore, the terms "part", "section", "portion", "member" or "element" when used in the singular can have the double meaning of a single part or a plurality of parts. As used herein to describe the above executive embodiment(s), the following directional terms "forward" , "backward" , "above" , "under", "vertical", "horizontal", "below" and "transverse", as well as any other similar directional term, refers to the embodiment described in the operating position. Finally, terms of degree such as "substantially", "about" and "approximately" as used herein are intended as a reasonable amount of deviation of the modified term such that the final result is not significantly changed.

While only selected embodiments have been chosen to illustrate the present invention, it will be apparent from this description to those skilled in the art that various modifications and variations can be made without departing from the scope of the invention as defined in the appended claims. For example, the sizes, shape, position or orientation of the various components can be modified as needed and/or desired. The components shown which are directly connected or in contact with each other can have intermediate structures arranged between them. The functions of one element can be performed by two and vice versa.

Claim 1:
A predictive and adaptive lighting control method comprising the step of
providing a controllable lighting system of a predetermined area (<NUM>, <NUM>), the method being characterized by the following steps:
- providing an artificial intelligence (<NUM>) based on sound, wherein said artificial intelligence based on sound is adapted to detect sound data and to transform the detected sound data into traffic data;
- performing a step of sound training of the artificial intelligence by means of images, comprising:
simultaneously detecting image data and sound data of a same scene,
classifying the detected image data based on object recognition, and
associating said detected sound data to the classified detected image data, thereby creating sample soundtracks (<NUM>);
- after the step of sound training by means of images:
a) performing at least one adaptive step, comprising:
a1) creating a traffic data history of said predetermined area based on detected sound data compared at least with the sample soundtracks (<NUM>);
a2) establishing at least lighting setting limits of said predetermined area based on the traffic data history;
b) performing at least one predictive step comprising:
b1) predicting local traffic events based at least on detected sound data compared at least with the sample soundtracks (<NUM>);
b2) controlling the lighting system of said predetermined area to adapt to the predicted local traffic events within the lighting setting limits established by the at least one adaptive step.