Patent Publication Number: US-2022234501-A1

Title: Alerting on Driving Affecting Signal

Description:
BACKGROUND 
     Playing loud music in a car, especially while driving may distract the driver and may increase the chances of a car accident. It is even illegal in various countries. 
     There is a need to reduce the risk associated with playing loud music in a car. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The embodiments of the disclosure will be understood and appreciated more fully from the following detailed description, taken in conjunction with the drawings in which: 
         FIG. 1A  illustrates an example of a method; 
         FIG. 1B  illustrates an example of a signature; 
         FIG. 1C  illustrates an example of a dimension expansion process; 
         FIG. 1D  illustrates an example of a merge operation; 
         FIG. 1E  illustrates an example of hybrid process; 
         FIG. 1F  illustrates an example of a method; 
         FIG. 1G  illustrates an example of a method; 
         FIG. 1H  illustrates an example of a method; 
         FIG. 1I  illustrates an example of a method; 
         FIG. 1J  illustrates an example of a method; 
         FIG. 1K  illustrates an example of a method; 
         FIG. 1L  illustrates an example of a method; 
         FIG. 1M  illustrates an example of a system; 
         FIG. 1N  is a partly-pictorial, partly-block diagram illustration of an exemplary obstacle detection and mapping system, constructed and operative in accordance with embodiments described herein; 
         FIG. 2A  is an example of a situation; 
         FIG. 2B  is an example of a situation; 
         FIG. 3A  is an example of a vehicle; 
         FIG. 3B  is an example of a vehicle; 
         FIG. 3C  is an example of a vehicle; 
         FIG. 4A  is an example of a method; 
         FIG. 4B  is an example of a method; and 
         FIG. 4C  is an example of a method. 
     
    
    
     DESCRIPTION OF EXAMPLE EMBODIMENTS 
     In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of the invention. However, it will be understood by those skilled in the art that the present invention may be practiced without these specific details. In other instances, well-known methods, procedures, and components have not been described in detail so as not to obscure the present invention. 
     The subject matter regarded as the invention is particularly pointed out and distinctly claimed in the concluding portion of the specification. The invention, however, both as to organization and method of operation, together with objects, features, and advantages thereof, may best be understood by reference to the following detailed description when read with the accompanying drawings. 
     It will be appreciated that for simplicity and clarity of illustration, elements shown in the figures have not necessarily been drawn to scale. For example, the dimensions of some of the elements may be exaggerated relative to other elements for clarity. Further, where considered appropriate, reference numerals may be repeated among the figures to indicate corresponding or analogous elements. 
     Because the illustrated embodiments of the present invention may for the most part, be implemented using electronic components and circuits known to those skilled in the art, details will not be explained in any greater extent than that considered necessary as illustrated above, for the understanding and appreciation of the underlying concepts of the present invention and in order not to obfuscate or distract from the teachings of the present invention. 
     Any reference in the specification to a method should be applied mutatis mutandis to a device or computerized system capable of executing the method and/or to a non-transitory computer readable medium that stores instructions for executing the method. 
     Any reference in the specification to a computerized system or device should be applied mutatis mutandis to a method that may be executed by the computerized system, and/or may be applied mutatis mutandis to non-transitory computer readable medium that stores instructions executable by the computerized system. 
     Any reference in the specification to a non-transitory computer readable medium should be applied mutatis mutandis to a device or computerized system capable of executing instructions stored in the non-transitory computer readable medium and/or may be applied mutatis mutandis to a method for executing the instructions. 
     Any combination of any module or unit listed in any of the figures, any part of the specification and/or any claims may be provided. 
     The specification and/or drawings may refer to an image. An image is an example of a media unit. Any reference to an image may be applied mutatis mutandis to a media unit. A media unit may be an example of sensed information unit. Any reference to a media unit may be applied mutatis mutandis to sensed information. The sensed information may be sensed by any type of sensors—such as a visual light camera, or a sensor that may sense infrared, radar imagery, ultrasound, electro-optics, radiography, LIDAR (light detection and ranging), etc. 
     The specification and/or drawings may refer to a processor. The processor may be a processing circuitry. The processing circuitry may be implemented as a central processing unit (CPU), and/or one or more other integrated circuits such as application-specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), full-custom integrated circuits, etc., or a combination of such integrated circuits. 
     Any combination of any steps of any method illustrated in the specification and/or drawings may be provided. 
     Any combination of any subject matter of any of claims may be provided. 
     Any combinations of computerized systems, units, components, processors, sensors, illustrated in the specification and/or drawings may be provided. 
     Any reference to any of the term “comprising” may be applied mutatis mutandis to the terms “consisting” and “consisting essentially of”. 
     Any reference to any of the term “consisting” may be applied mutatis mutandis to the terms “comprising” and “consisting essentially of”. 
     Any reference to any of the term “consisting essentially of” may be applied mutatis mutandis to the terms “comprising” and “comprising”. 
     Audio generated outside the vehicle may include driving affecting audio. Driving affecting audio is audio that may affect a manner in which a vehicle is driven. Driving affecting audio may be for example, any audio generated by an entity (being human or non-human) that may affect the driving of the vehicle. Affect may include should be taken into account when driving. Non-limiting example are audio alerts, audio that was found (for example learnt) as preceding accidents or near accidents, sounds of brakes, horns, shouts, a sound of an approaching train, and the like. The driving affecting audio may be aimed to a driver (for example—shouts, alerts, horns) or not directly aimed to the driver (braking sounds), and the like. What amounts to a driving affecting audio may be learnt in any manner, may be provided to the system executing any of the following methods in any manner, may change over time, may be determined based on feedback, and the like. 
       FIGS. 2A and 2B  illustrate examples in which vehicle  51  is in situations that may cause other entities outside the vehicle to generate audio that may include driving affecting audio (denoted  59 ). 
     In  FIG. 2A , vehicle  51  reaches a zebra cross located at excessive speed—which may trigger a generation of driving affecting audio  59  by a person that crosses the zebra crossing, one or more other pedestrian, a vehicle  52  following vehicle  51  and/or another vehicle  53  that drives at an opposite lane of vehicle  51 . 
     In  FIG. 2B , vehicle  51  deviates to an opposite lane and this may a generation of driving affecting audio  59  by another vehicle  53  that drives at an opposite lane of vehicle  51 . 
       FIGS. 3A, 3B and 3C  illustrates examples of vehicle  51  and driving affecting audio  59  generated outside the vehicle. Driver  58  is located within the vehicle. 
     The vehicle includes sensing unit  60  that may include one or more audio sensors (in  FIG. 3C  the sensing unit includes audio sensors  61  for sensing audio generated outside the vehicle and a in-vehicle sensor  62  for sensing the audio near driver  58 ). Any of the sensing unit  61  may include any other type of sensors. 
     The vehicle also includes a decision unit  70  that may include a processor and/or any other components, that may be configured to determine whether the audio generated outside the vehicle includes driving affecting audio. 
     The vehicle may include generator  80  for generating at least one of the in vehicle indicator (denoted  69  in  FIGS. 3A and 3C ) or for generating a request, a trigger or a command (denoted  69 ′ in  FIG. 3B ) to another device (such a driver wearable device  57 ) to generate the in-vehicle indication. 
       FIG. 4A  illustrates method  101  for providing an in-vehicle indication. 
     Method  100  start by step  110  of obtaining, during a driving session of a vehicle, external audio information about audio generated outside the vehicle. 
     Step  110  may include sensing the audio generated outside the vehicle and generating the external audio information and/or receiving sensed information indicative of the audio generated outside the vehicle, and the like. Step  110  may be executed by one or more audio sensors located in any location—for example—external audio sensors facing the exterior of the vehicle, or acoustically coupled to the exterior of the vehicle. 
     Step  110  may be followed by step  120  of determining whether the audio generated outside the vehicle includes driving affecting audio. The determination can be made in any manner, for example searching for signatures of driving affecting audio, searching in any manner that presence of driving affecting audio, applying a machine learning process trained to locate the driving affecting audio, and the like. An example for detecting certain media unit content is illustrated in PCT patent application PCT/IB2019/058207 which is incorporated herein by reference. 
     Step  120  may be followed by step  130  of generating the in-vehicle indication, within the vehicle, when determining that the audio generated outside comprises driving affecting audio. The in-vehicle indication is indicative about the driving affecting audio generated outside the vehicle. 
     The in-vehicle indication may be provided in any form—audio, visual, audio-visual, tactile, or any other signal. Tactile alerts may be provided by contacting the driver in a controlled manner. 
     The driving relevant in-vehicle audio may be reconstructed version of the driving affecting audio. Reconstructed—may include audio components of the driving affecting audio, or may be processed in any other manner—for example by applying filtering, noise reduction, and the like. 
     Step  130  may take into account various parameters—such as other signals that may be presented to the driver (for example—other sounds that are heard by the driver, the manner in which the driver is receiving other signals—using earphones, using a vehicle media device, the loudness other audio signals heard by the driver, and the like). 
     Step  130  may include step  131  of estimating at least one other sound (for example—music, podcast, radio station) to be generated within the vehicle, when the driving relevant in-vehicle audio is played in the vehicle, wherein the at least one other sound differs from the driving relevant in-vehicle audio. 
     Step  130  may include step  132  of generating the driving relevant in-vehicle audio to be heard by at least the driver at the presence of the at least one other sound. Thus—the in-vehicle audio should overcome the at least one other sound. 
       FIG. 4B  illustrates method  102  for providing an in-vehicle indication. 
     Method  101  start by step  110  of obtaining, during a driving session of a vehicle, external audio information about audio generated outside the vehicle. 
     Step  110  may be followed by step  120  of determining whether the audio generated outside the vehicle includes driving affecting audio. 
     Step  120  may be followed by step  134  of providing a request, a trigger or a command to another device to generate the in-vehicle indication. 
     The request, trigger a command can be sent to another device that in turn may generate the in-vehicle indication. 
     The other device may be a part of the vehicle, a multimedia device, a speaker of the vehicle, the other device may be positioned (at least temporarily) within the vehicle, may belong to or used by the driver (for example earphones, mobile phone, mobile computer, smart phone), and the like. 
     Step  134  may also include requesting, commanding or triggering the other device to change at least one other signal outputted by the other device when the in-vehicle indication is generated or in timing proximity (for example a few seconds) to the transmission of the in-vehicle indication. 
     For example—the other device may be requested, triggered to or instructed to generate only the in-vehicle indication, to attenuate other output signals when the in-vehicle indication is outputted, and the like. 
       FIG. 4C  illustrates method  103  for providing an in-vehicle indication. 
     Method  103  start by step  110  of obtaining, during a driving session of a vehicle, external audio information about audio generated outside the vehicle. 
     Step  110  may be followed by step  120  of determining whether the audio generated outside the vehicle includes driving affecting audio. 
     Step  120  may be followed by step  140  of determining whether to generate the in-vehicle indication. 
     Step  140  may include determining not to generate the in-vehicle indication based on a relevancy of the generated in-vehicle indication. 
     For example—step  140  may determine not to generate the in-vehicle indication when the driving affecting audio is heard (or at least sufficiently heard) by the driver. What amounts to sufficiently heard can be determined in any manner—for example be of at least a predefined volume, exhibit at least a predefined signal to noise ratio, being noticed by the driver, and the like). 
     Yet for another example—step  140  may determine not to generate the in-vehicle indication when the vehicle is driving in an autonomous mode. 
     Step  140  may be followed by step  130  when determining to generate the in-vehicle indication. Step  140  may be followed by step  134  of  FIG. 4C , and the like. 
     Low Power Generation of Signatures 
     The analysis of content of a media unit may be executed by generating a signature of the media unit and by comparing the signature to reference signatures. The reference signatures may be arranged in one or more concept structures or may be arranged in any other manner. The signatures may be used for object detection or for any other use. 
     The signature may be generated by creating a multidimensional representation of the media unit. The multidimensional representation of the media unit may have a very large number of dimensions. The high number of dimensions may guarantee that the multidimensional representation of different media units that include different objects is sparse—and that object identifiers of different objects are distant from each other—thus improving the robustness of the signatures. 
     The generation of the signature is executed in an iterative manner that includes multiple iterations, each iteration may include an expansion operations that is followed by a merge operation. The expansion operation of an iteration is performed by spanning elements of that iteration. By determining, per iteration, which spanning elements (of that iteration) are relevant—and reducing the power consumption of irrelevant spanning elements—a significant amount of power may be saved. 
     In many cases, most of the spanning elements of an iteration are irrelevant—thus after determining (by the spanning elements) their relevancy—the spanning elements that are deemed to be irrelevant may be shut down a/or enter an idle mode. 
       FIG. 1A  illustrates a method  5000  for generating a signature of a media unit. 
     Method  5000  may start by step  5010  of receiving or generating sensed information. 
     The sensed information may be a media unit of multiple objects. 
     Step  5010  may be followed by processing the media unit by performing multiple iterations, wherein at least some of the multiple iterations comprises applying, by spanning elements of the iteration, dimension expansion process that are followed by a merge operation. 
     The processing may include: 
     Step  5020  of performing a k&#39;th iteration expansion process (k may be a variable that is used to track the number of iterations). 
     Step  5030  of performing a k&#39;th iteration merge process. 
     Step  5040  of changing the value of k. 
     Step  5050  of checking if all required iterations were done—if so proceeding to step  5060  of completing the generation of the signature. Else—jumping to step  5020 . 
     The output of step  5020  is a k&#39;th iteration expansion results  5120 . 
     The output of step  5030  is a k&#39;th iteration merge results  5130 . 
     For each iteration (except the first iteration)—the merge result of the previous iteration is an input to the current iteration expansion process. 
     At least some of the K iterations involve selectively reducing the power consumption of some spanning elements (during step  5020 ) that are deemed to be irrelevant. 
       FIG. 1B  is an example of an image signature  6027  of a media unit that is an image  6000  and of an outcome  6013  of the last (K&#39;th) iteration. 
     The image  6001  is virtually segments to segments  6000 ( i,k ). The segments may be of the same shape and size but this is not necessarily so. 
     Outcome  6013  may be a tensor that includes a vector of values per each segment of the media unit. One or more objects may appear in a certain segment. For each object—an object identifier (of the signature) points to locations of significant values, within a certain vector associated with the certain segment. 
     For example—a top left segment ( 6001 ( 1 , 1 )) of the image may be represented in the outcome  6013  by a vector V( 1 , 1 )  6017 ( 1 , 1 ) that has multiple values. The number of values per vector may exceed 100, 200, 500, 1000, and the like. 
     The significant values (for example—more than 10, 20, 30, 40 values, and/or more than 0.1%, 0.2%. 0.5%, 1%, 5% of all values of the vector and the like) may be selected. The significant values may have the values—but may be selected in any other manner. 
       FIG. 1B  illustrates a set of significant responses  6015 ( 1 , 1 ) of vector V( 1 , 1 )  6017 ( 1 , 1 ). The set includes five significant values (such as first significant value SV 1 ( 1 , 1 )  6013 ( 1 , 1 , 1 ), second significant value SV 2 ( 1 , 1 ), third significant value SV 3 ( 1 , 1 ), fourth significant value SV 4 ( 1 , 1 ), and fifth significant value SV 5 ( 1 , 1 )  6013 ( 1 , 1 , 5 ). 
     The image signature  6027  includes five indexes for the retrieval of the five significant values—first till fifth identifiers ID 1 -ID 5  are indexes for retrieving the first till fifth significant values. 
       FIG. 1C  illustrates a k&#39;th iteration expansion process. 
     The k&#39;th iteration expansion process start by receiving the merge results  5060 ′ of a previous iteration. 
     The merge results of a previous iteration may include values are indicative of previous expansion processes—for example—may include values that are indicative of relevant spanning elements from a previous expansion operation, values indicative of relevant regions of interest in a multidimensional representation of the merge results of a previous iteration. 
     The merge results (of the previous iteration) are fed to spanning elements such as spanning elements  5061 ( 1 )- 5061 (J). 
     Each spanning element is associated with a unique set of values. The set may include one or more values. The spanning elements apply different functions that may be orthogonal to each other. Using non-orthogonal functions may increase the number of spanning elements—but this increment may be tolerable. 
     The spanning elements may apply functions that are decorrelated to each other—even if not orthogonal to each other. 
     The spanning elements may be associated with different combinations of object identifiers that may “cover” multiple possible media units. Candidates for combinations of object identifiers may be selected in various manners—for example based on their occurrence in various images (such as test images) randomly, pseudo randomly, according to some rules and the like. Out of these candidates the combinations may be selected to be decorrelated, to cover said multiple possible media units and/or in a manner that certain objects are mapped to the same spanning elements. 
     Each spanning element compares the values of the merge results to the unique set (associated with the spanning element) and if there is a match—then the spanning element is deemed to be relevant. If so—the spanning element completes the expansion operation. 
     If there is no match—the spanning element is deemed to be irrelevant and enters a low power mode. The low power mode may also be referred to as an idle mode, a standby mode, and the like. The low power mode is termed low power because the power consumption of an irrelevant spanning element is lower than the power consumption of a relevant spanning element. 
     In  FIG. 1C  various spanning elements are relevant ( 5061 ( 1 )- 5061 ( 3 )) and one spanning element is irrelevant ( 5061 (J)). 
     Each relevant spanning element may perform a spanning operation that includes assigning an output value that is indicative of an identity of the relevant spanning elements of the iteration. The output value may also be indicative of identities of previous relevant spanning elements (from previous iterations). 
     For example—assuming that spanning element number fifty is relevant and is associated with a unique set of values of eight and four—then the output value may reflect the numbers fifty, four and eight—for example one thousand multiplied by (fifty+forty) plus forty. Any other mapping function may be applied. 
       FIG. 1C  also illustrates the steps executed by each spanning element: 
     Checking if the merge results are relevant to the spanning element (step  5091 ). 
     If—so—completing the spanning operation (step  5093 ). 
     If not—entering an idle state (step  5092 ). 
       FIG. 1D  is an example of various merge operations. 
     A merge operation may include finding regions of interest. The regions of interest are regions within a multidimensional representation of the sensed information. A region of interest may exhibit a more significant response (for example a stronger, higher intensity response). 
     The merge operation (executed during a k&#39;th iteration merge operation) may include at least one of the following: 
     Step  5031  of searching for overlaps between regions of interest (of the k&#39;th iteration expansion operation results) and define regions of interest that are related to the overlaps. 
     Step  5032  of determining to drop one or more region of interest, and dropping according to the determination. 
     Step  5033  of searching for relationships between regions of interest (of the k&#39;th iteration expansion operation results) and define regions of interest that are related to the relationship. 
     Step  5034  of searching for proximate regions of interest (of the k&#39;th iteration expansion operation results) and define regions of interest that are related to the proximity Proximate may be a distance that is a certain fraction (for example less than 1%) of the multi-dimensional space, may be a certain fraction of at least one of the regions of interest that are tested for proximity. 
     Step  5035  of searching for relationships between regions of interest (of the k&#39;th iteration expansion operation results) and define regions of interest that are related to the relationship. 
     Step  5036  of merging and/or dropping k&#39;th iteration regions of interest based on shape information related to shape of the k&#39;th iteration regions of interest. 
     The same merge operations may applied in different iterations. 
     Alternatively, different merge operations may be executed during different iterations. 
       FIG. 1E  illustrates an example of a hybrid process and an input image  6001 . 
     The hybrid process is hybrid in the sense that some expansion and merge operations are executed by a convolutional neural network (CNN) and some expansion and merge operations (denoted additional iterations of expansion and merge) are not executed by the CNN— but rather by a process that may include determining a relevancy of spanning elements and entering irrelevant spanning elements to a low power mode. 
     In  FIG. 1E  one or more initial iterations are executed by first and second CNN layers  6010 ( 1 ) and  6010 ( 2 ) that apply first and second functions  6015 ( 1 ) and  6015 ( 2 ). 
     The output of these layers provided information about image properties. The image properties may not amount to object detection. Image properties may include location of edges, properties of curves, and the like. 
     The CNN may include additional layers (for example third till N&#39;th layer  6010 (N)) that may provide a CNN output  6018  that may include object detection information. It should be noted that the additional layers may not be included. 
     It should be noted that executing the entire signature generation process by a hardware CNN of fixed connectivity may have a higher power consumption—as the CNN will not be able to reduce the power consumption of irrelevant nodes. 
       FIG. 1F  illustrates a method  7000  for low-power calculation of a signature. 
     Method  7000  starts by step  7010  of receiving or generating a media unit of multiple objects. 
     Step  7010  may be followed by step  7012  of processing the media unit by performing multiple iterations, wherein at least some of the multiple iterations comprises applying, by spanning elements of the iteration, dimension expansion process that are followed by a merge operation. 
     The applying of the dimension expansion process of an iteration may include (a) determining a relevancy of the spanning elements of the iteration; and (b) completing the dimension expansion process by relevant spanning elements of the iteration and reducing a power consumption of irrelevant spanning elements until, at least, a completion of the applying of the dimension expansion process. 
     The identifiers may be retrieval information for retrieving the significant portions. 
     The at least some of the multiple iterations may be a majority of the multiple iterations. 
     The output of the multiple iteration may include multiple property attributes for each segment out of multiple segments of the media unit; and wherein the significant portions of an output of the multiple iterations may include more impactful property attributes. 
     The first iteration of the multiple iteration may include applying the dimension expansion process by applying different filters on the media unit. 
     The at least some of the multiple iteration exclude at least a first iteration of the multiple iterations. See, for example,  FIG. 1E . 
     The determining the relevancy of the spanning elements of the iteration may be based on at least some identities of relevant spanning elements of at least one previous iteration. 
     The determining the relevancy of the spanning elements of the iteration may be based on at least some identities of relevant spanning elements of at least one previous iteration that preceded the iteration. 
     The determining the relevancy of the spanning elements of the iteration may be based on properties of the media unit. 
     The determining the relevancy of the spanning elements of the iteration may be performed by the spanning elements of the iteration. 
     Method  7000  may include a neural network processing operation that may be executed by one or more layers of a neural network and does not belong to the at least some of the multiple iterations. See, for example,  FIG. 1E . 
     The at least one iteration may be executed without reducing power consumption of irrelevant neurons of the one or more layers. 
     The one or more layers may output information about properties of the media unit, wherein the information differs from a recognition of the multiple objects. 
     The applying, by spanning elements of an iteration that differs from the first iteration, the dimension expansion process may include assigning output values that may be indicative of an identity of the relevant spanning elements of the iteration. See, for example,  FIG. 1C . 
     The applying, by spanning elements of an iteration that differs from the first iteration, the dimension expansion process may include assigning output values that may be indicative a history of dimension expansion processes until the iteration that differs from the first iteration. 
     The each spanning element may be associated with a subset of reference identifiers. The determining of the relevancy of each spanning elements of the iteration may be based a relationship between the subset of the reference identifiers of the spanning element and an output of a last merge operation before the iteration. 
     The output of a dimension expansion process of an iteration may be a multidimensional representation of the media unit that may include media unit regions of interest that may be associated with one or more expansion processes that generated the regions of interest. 
     The merge operation of the iteration may include selecting a subgroup of media unit regions of interest based on a spatial relationship between the subgroup of multidimensional regions of interest. 
     Method  7000  may include applying a merge function on the subgroup of multidimensional regions of interest. See, for example,  FIG. 1C . 
     Method  7000  may include applying an intersection function on the subgroup of multidimensional regions of interest. See, for example,  FIG. 1C . 
     The merge operation of the iteration may be based on an actual size of one or more multidimensional regions of interest. 
     The merge operation of the iteration may be based on relationship between sizes of the multidimensional regions of interest. For example—larger multidimensional regions of interest may be maintained while smaller multidimensional regions of interest may be ignored of. 
     The merge operation of the iteration may be based on changes of the media unit regions of interest during at least the iteration and one or more previous iteration. 
     Step  7012  may be followed by step  7014  of determining identifiers that are associated with significant portions of an output of the multiple iterations. 
     Step  7014  may be followed by step  7016  of providing a signature that comprises the identifiers and represents the multiple objects. 
     Localization and Segmentation 
     Any of the mentioned above signature generation method provides a signature that does not explicitly includes accurate shape information. This adds to the robustness of the signature to shape related inaccuracies or to other shape related parameters. 
     The signature includes identifiers for identifying media regions of interest. 
     Each media region of interest may represent an object (for example a vehicle, a pedestrian, a road element, a human made structure, wearables, shoes, a natural element such as a tree, the sky, the sun, and the like) or a part of an object (for example—in the case of the pedestrian—a neck, a head, an arm, a leg, a thigh, a hip, a foot, an upper arm, a forearm, a wrist, and a hand). It should be noted that for object detection purposes a part of an object may be regarded as an object. 
     The exact shape of the object may be of interest. 
       FIG. 1G  illustrates method  7002  of generating a hybrid representation of a media unit. 
     Method  7002  may include a sequence of steps  7020 ,  7022 ,  7024  and  7026 . 
     Step  7020  may include receiving or generating the media unit. 
     Step  7022  may include processing the media unit by performing multiple iterations, wherein at least some of the multiple iterations comprises applying, by spanning elements of the iteration, dimension expansion process that are followed by a merge operation. 
     Step  7024  may include selecting, based on an output of the multiple iterations, media unit regions of interest that contributed to the output of the multiple iterations. 
     Step  7026  may include providing a hybrid representation, wherein the hybrid representation may include (a) shape information regarding shapes of the media unit regions of interest, and (b) a media unit signature that includes identifiers that identify the media unit regions of interest. 
     Step  7024  may include selecting the media regions of interest per segment out of multiple segments of the media unit. See, for example,  FIG. 2 . 
     Step  7026  may include step  7027  of generating the shape information. 
     The shape information may include polygons that represent shapes that substantially bound the media unit regions of interest. These polygons may be of a high degree. 
     In order to save storage space, the method may include step  7028  of compressing the shape information of the media unit to provide compressed shape information of the media unit. 
       FIG. 1H  illustrates method  5002  for generating a hybrid representation of a media unit. 
     Method  5002  may start by step  5011  of receiving or generating a media unit. 
     Step  5011  may be followed by processing the media unit by performing multiple iterations, wherein at least some of the multiple iterations comprises applying, by spanning elements of the iteration, dimension expansion process that are followed by a merge operation. 
     The processing may be followed by steps  5060  and  5062 . 
     The processing may include steps  5020 ,  5030 ,  5040  and  5050 . 
     Step  5020  may include performing a k&#39;th iteration expansion process (k may be a variable that is used to track the number of iterations). 
     Step  5030  may include performing a k&#39;th iteration merge process. 
     Step  5040  may include changing the value of k. 
     Step  5050  may include checking if all required iterations were done—if so proceeding to steps  5060  and  5062 . Else—jumping to step  5020 . 
     The output of step  5020  is a k&#39;th iteration expansion result. 
     The output of step  5030  is a k&#39;th iteration merge result. 
     For each iteration (except the first iteration)—the merge result of the previous iteration is an input to the current iteration expansion process. 
     Step  5060  may include completing the generation of the signature. 
     Step  5062  may include generating shape information regarding shapes of media unit regions of interest. The signature and the shape information provide a hybrid representation of the media unit. 
     The combination of steps  5060  and  5062  amounts to a providing a hybrid representation, wherein the hybrid representation may include (a) shape information regarding shapes of the media unit regions of interest, and (b) a media unit signature that includes identifiers that identify the media unit regions of interest. 
     Object Detection Using Compressed Shape Information 
     Object detection may include comparing a signature of an input image to signatures of one or more cluster structures in order to find one or more cluster structures that include one or more matching signatures that match the signature of the input image. 
     The number of input images that are compared to the cluster structures may well exceed the number of signatures of the cluster structures. For example—thousands, tens of thousands, hundreds of thousands (and even more) of input signature may be compared to much less cluster structure signatures. The ratio between the number of input images to the aggregate number of signatures of all the cluster structures may exceed ten, one hundred, one thousand, and the like. 
     In order to save computational resources, the shape information of the input images may be compressed. 
     On the other hand—the shape information of signatures that belong to the cluster structures may be uncompressed—and of higher accuracy than those of the compressed shape information. 
     When the higher quality is not required—the shape information of the cluster signature may also be compressed. 
     Compression of the shape information of cluster signatures may be based on a priority of the cluster signature, a popularity of matches to the cluster signatures, and the like. 
     The shape information related to an input image that matches one or more of the cluster structures may be calculated based on shape information related to matching signatures. 
     For example—a shape information regarding a certain identifier within the signature of the input image may be determined based on shape information related to the certain identifiers within the matching signatures. 
     Any operation on the shape information related to the certain identifiers within the matching signatures may be applied in order to determine the (higher accuracy) shape information of a region of interest of the input image identified by the certain identifier. 
     For example—the shapes may be virtually overlaid on each other and the population per pixel may define the shape. 
     For example—only pixels that appear in at least a majority of the overlaid shaped should be regarded as belonging to the region of interest. 
     Other operations may include smoothing the overlaid shapes, selecting pixels that appear in all overlaid shapes. 
     The compressed shape information may be ignored of or be taken into account. 
       FIG. 1I  illustrates a matching process and a generation of a higher accuracy shape information. 
     It is assumed that there are multiple (M) cluster structures  4974 ( 1 )- 4974 (M). Each cluster structure includes cluster signatures, metadata regarding the cluster signatures, and shape information regarding the regions of interest identified by identifiers of the cluster signatures. 
     For example—first cluster structure  4974 ( 1 ) includes multiple (N 1 ) signatures (referred to as cluster signatures CS) CS( 1 , 1 )-CS( 1 ,N 1 )  4975 ( 1 , 1 )- 4975 ( 1 ,N 1 ), metadata  4976 ( 1 ), and shape information (Shapeinfo  4977 ( 1 )) regarding shapes of regions of interest associated with identifiers of the CSs. 
     Yet for another example—M&#39;th cluster structure  4974 (M) includes multiple (N 2 ) signatures (referred to as cluster signatures CS) CS(M, 1 )-CS(M,N 2 )  4975 (M, 1 )- 4975 (M,N 2 ), metadata  4976 (M), and shape information (Shapeinfo  4977 (M)) regarding shapes of regions of interest associated with identifiers of the CSs. 
     The number of signatures per concept structure may change over time—for example due to cluster reduction attempts during which a CS is removed from the structure to provide a reduced cluster structure, the reduced structure is checked to determine that the reduced cluster signature may still identify objects that were associated with the (non-reduced) cluster signature—and if so the signature may be reduced from the cluster signature. 
     The signatures of each cluster structures are associated to each other, wherein the association may be based on similarity of signatures and/or based on association between metadata of the signatures. 
     Assuming that each cluster structure is associated with a unique object—then objects of a media unit may be identified by finding cluster structures that are associated with said objects. The finding of the matching cluster structures may include comparing a signature of the media unit to signatures of the cluster structures- and searching for one or more matching signature out of the cluster signatures. 
     In  FIG. 1I —a media unit having a hybrid representation undergoes object detection. The hybrid representation includes media unit signature  4972  and compressed shape information  4973 . 
     The media unit signature  4972  is compared to the signatures of the M cluster structures—from CS( 1 , 1 )  4975 ( 1 , 1 ) till CS(M,N 2 )  4975 (M,N 2 ). 
     We assume that one or more cluster structures are matching cluster structures. 
     Once the matching cluster structures are found the method proceeds by generating shape information that is of higher accuracy then the compressed shape information. 
     The generation of the shape information is done per identifier. 
     For each j that ranges between 1 and J (J is the number of identifiers per the media unit signature  4972 ) the method may perform the steps of: 
     Find (step  4978 ( j )) the shape information of the j&#39;th identifier of each matching signature- or of each signature of the matching cluster structure. 
     Generate (step  4979 ( j )) a higher accuracy shape information of the j&#39;th identifier. 
     For example—assuming that the matching signatures include CS( 1 , 1 )  2975 ( 1 , 1 ), CS( 2 , 5 )  2975 ( 2 , 5 ), CS( 7 , 3 )  2975 ( 7 , 3 ) and CS( 15 , 2 )  2975 ( 15 , 2 ), and that the j&#39;th identifier is included in CS( 1 , 1 )  2975 ( 1 , 1 ),CS( 7 , 3 )  2975 ( 7 , 3 ) and CS( 15 , 2 )  2975 ( 15 , 2 )—then the shape information of the j&#39;th identifier of the media unit is determined based on the shape information associated with CS( 1 , 1 )  2975 ( 1 , 1 ),CS( 7 , 3 )  2975 ( 7 , 3 ) and CS( 15 , 2 )  2975 ( 15 , 2 ). 
       FIG. 1P  illustrates an image  8000  that includes four regions of interest  8001 ,  8002 ,  8003  and  8004 . The signature  8010  of image  8000  includes various identifiers including ID 1   8011 , ID 2   8012 , ID 3   8013  and ID 4   8014  that identify the four regions of interest  8001 ,  8002 ,  8003  and  8004 . 
     The shapes of the four regions of interest  8001 ,  8002 ,  8003  and  8004  are four polygons. Accurate shape information regarding the shapes of these regions of interest may be generated during the generation of signature  8010 . 
       FIG. 1J  illustrates method  8030  for object detection. 
     Method  8030  may include the steps of method  8020  or may be preceded by steps  8022 ,  8024  and  8026 . 
     Method  8030  may include a sequence of steps  8032 ,  8034 ,  8036  and  8038 . 
     Step  8032  may include receiving or generating an input image. 
     Step  8034  may include generating a signature of the input image. 
     Step  8036  may include comparing the signature of the input image to signatures of a certain concept structure. The certain concept structure may be generated by method  8020 . 
     Step  8038  may include determining that the input image comprises the object when at least one of the signatures of the certain concept structure matches the signature of the input image. 
       FIG. 2D  illustrates method  8040  for object detection. 
     Method  8040  may include the steps of method  8020  or may be preceded by steps  8022 ,  8024  and  8026 . 
     Method  8040  may include a sequence of steps  8041 ,  8043 ,  8045 ,  8047  and  8049 . 
     Step  8041  may include receiving or generating an input image. 
     Step  8043  may include generating a signature of the input image, the signature of the input image comprises only some of the certain second image identifiers; wherein the input image of the second scale. 
     Step  8045  may include changing a scale of the input image to the first scale to a provide an amended input image. 
     Step  8047  may include generating a signature of the amended input image. 
     Step  8049  may include verifying that the input image comprises the object when the signature of the amended input image comprises the at least one certain first image identifier. 
     Object Detection that is Robust to Angle of Acquisition 
     Object detection may benefit from being robust to the angle of acquisition—to the angle between the optical axis of an image sensor and a certain part of the object. This allows the detection process to be more reliable, use fewer different clusters (may not require multiple clusters for identifying the same object from different images). 
       FIG. 1K  illustrates method  8120  that includes the following steps: 
     Step  8122  of receiving or generating images of objects taken from different angles. 
     Step  8124  of finding images of objects taken from different angles that are close to each other. Close enough may be less than 1,5,10,15 and 20 degrees—but the closeness may be better reflected by the reception of substantially the same signature. 
     Step  8126  of linking between the images of similar signatures. This may include searching for local similarities. The similarities are local in the sense that they are calculated per a subset of signatures. For example—assuming that the similarity is determined per two images—then a first signature may be linked to a second signature that is similar to the first image. A third signature may be linked to the second image based on the similarity between the second and third signatures- and even regardless of the relationship between the first and third signatures. 
     Step  8126  may include generating a concept data structure that includes the similar signatures. 
     This so-called local or sliding window approach, in addition to the acquisition of enough images (that will statistically provide a large angular coverage) will enable to generate a concept structure that include signatures of an object taken at multiple directions. 
     Signature Tailored Matching Threshold 
     Object detection may be implemented by (a) receiving or generating concept structures that include signatures of media units and related metadata, (b) receiving a new media unit, generating a new media unit signature, and (c) comparing the new media unit signature to the concept signatures of the concept structures. 
     The comparison may include comparing new media unit signature identifiers (identifiers of objects that appear in the new media unit) to concept signature identifiers and determining, based on a signature matching criteria whether the new media unit signature matches a concept signature. If such a match is found then the new media unit is regarded as including the object associated with that concept structure. 
     It was found that by applying an adjustable signature matching criteria, the matching process may be highly effective and may adapt itself to the statistics of appearance of identifiers in different scenarios. For example—a match may be obtained when a relatively rear but highly distinguishing identifier appears in the new media unit signature and in a cluster signature, but a mismatch may be declared when multiple common and slightly distinguishing identifiers appear in the new media unit signature and in a cluster signature. 
       FIG. 1L  illustrates method  8200  for object detection. 
     Method  8200  may include: 
     Step  8210  of receiving an input image. 
     Step  8212  of generating a signature of the input image. 
     Step  8214  of comparing the signature of the input image to signatures of a concept structure. 
     Step  8216  of determining whether the signature of the input image matches any of the signatures of the concept structure based on signature matching criteria, wherein each signature of the concept structure is associated within a signature matching criterion that is determined based on an object detection parameter of the signature. 
     Step  8218  of concluding that the input image comprises an object associated with the concept structure based on an outcome of the determining. 
     The signature matching criteria may be a minimal number of matching identifiers that indicate of a match. For example—assuming a signature that include few tens of identifiers, the minimal number may vary between a single identifier to all of the identifiers of the signature. 
     It should be noted that an input image may include multiple objects and that an signature of the input image may match multiple cluster structures. Method  8200  is applicable to all of the matching processes- and that the signature matching criteria may be set for each signature of each cluster structure. 
     Step  8210  may be preceded by step  8202  of determining each signature matching criterion by evaluating object detection capabilities of the signature under different signature matching criteria. 
     Step  8202  may include: 
     Step  8203  of receiving or generating signatures of a group of test images. 
     Step  8204  of calculating the object detection capability of the signature, for each signature matching criterion of the different signature matching criteria. 
     Step  8206  of selecting the signature matching criterion based on the object detection capabilities of the signature under the different signature matching criteria. 
     The object detection capability may reflect a percent of signatures of the group of test images that match the signature. 
     The selecting of the signature matching criterion comprises selecting the signature matching criterion that once applied results in a percent of signatures of the group of test images that match the signature that is closets to a predefined desired percent of signatures of the group of test images that match the signature. 
     The object detection capability may reflect a significant change in the percent of signatures of the group of test images that match the signature. For example—assuming, that the signature matching criteria is a minimal number of matching identifiers and that changing the value of the minimal numbers may change the percentage of matching test images. A substantial change in the percentage (for example a change of more than 10, 20, 30, 40 percent) may be indicative of the desired value. The desired value may be set before the substantial change, proximate to the substantial change, and the like. 
     For example, referring to  FIG. 1I , cluster signatures CS( 1 , 1 ), CS( 2 , 5 ), CS( 7 , 3 ) and CS( 15 , 2 ) match unit signature  4972 . Each of these matches may apply a unique signature matching criterion. 
     Examples of Systems 
       FIG. 21M  illustrates an example of a system capable of executing one or more of the mentioned above methods. 
     The system include various components, elements and/or units. 
     A component element and/or unit may be a processing circuitry may be implemented as a central processing unit (CPU), and/or one or more other integrated circuits such as application-specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), full-custom integrated circuits, etc., or a combination of such integrated circuits. 
     Alternatively, each component element and/or unit may implemented in hardware, firmware, or software that may be executed by a processing circuitry. 
     System  4900  may include sensing unit  4902 , communication unit  4904 , input  4911 , one or more processors—such as processor  4950 , and output  4919 . The communication unit  4904  may include the input and/or the output. The communication unit  4904  may communicate with any entity—within the vehicle (for example driver device, passenger device, multimedia device), outside the vehicle (another vehicle, another computerized system—such as out-of-vehicle computerized system  4820  of  FIG. 1N , another road user, another human outside the vehicle), and the like. 
     Input and/or output may be any suitable communications component such as a network interface card, universal serial bus (USB) port, disk reader, modem or transceiver that may be operative to use protocols such as are known in the art to communicate either directly, or indirectly, with other elements of the system. 
     Processor  4950  may include at least some out of (and thus may not include at least one out of):
         Multiple spanning elements  4951 ( q ).   Multiple merge elements  4952 ( r ).   Object detector  4953 .   Cluster manager  4954 .   Controller  4955 .   Selection unit  4956 .   Object detection determination unit  4957 .   Signature generator  4958 .   Movement information unit  4959 .   Identifier unit  4960 .       

     While system  4900  includes a sensing unit  4902 —is should be noted that it may receive sensed information from other sensors and/or that the sensing unit does not belong to the system. The system may receive information from one or more sensors located in the vehicle, associated with the vehicle, and/or located outside the vehicle. 
     Any method illustrated in the specification may be fully or partially executed by system  4900 , and/or may be fully or partially executed by one or more other computerized system, and/or by one or more computerized systems—for example by task allocations between computerized systems, by a cooperation (for example—exchange of information, exchange of decisions, any allocation of resources, collaborative decision, and the like) between multiple computerized systems. 
     The one or more other computerized systems may be, for example, out-of-vehicle computerized system  4820  of  FIG. 1N , any other out-of-vehicle computerized system, one or more other in-vehicle systems, a computerized device of a person within the vehicle, any computerized system outside the vehicle—including for example a computerized system of another vehicle. 
     An example of an other in-vehicle system is denoted  4830  in  FIG. 1N  and is located within vehicle  4800  that drives along road  4820 . 
     System  4900  may obtain sensed information from any type of sensors—a camera, one or more sensors implemented using any suitable imaging technology instead of, or in addition to, a conventional camera, an infrared sensor, a radar, an ultrasound sensor, any electro-optic sensor, a radiography sensor, a LIDAR (light detection and ranging), telemetry ECU sensor, shock sensor, etc. 
     System  4900  and/or other in-vehicle system is denoted  4830  may use supervised and/or unsupervised learning to perform any method executed by them. 
     The other in-vehicle system  4830  may be an autonomous driving system, an advance driver assistance system, or may differ from an autonomous driving system and from an advance driver assistance system. 
     The other in-vehicle system  4830  may include processing circuitry  210 , input/output (I/O) module  220 , one or more sensors  233 , and database  270 . The processing circuitry  210  may execute any task is it assigned or programmed to perform in relation to any of the methods illustrate din the application. Alternatively—the other in-vehicle system  4830  may include another module for executing (alone or with the processing circuit) any such task. For example—the processing circuitry may execute instructions to provide an autonomous driving manager functionality. Alternatively—another circuit or module of the in-vehicle system  4830  may provide the autonomous driving manager functionality. 
     While the foregoing written description of the invention enables one of ordinary skill to make and use what is considered presently to be the best mode thereof, those of ordinary skill will understand and appreciate the existence of variations, combinations, and equivalents of the specific embodiment, method, and examples herein. The invention should therefore not be limited by the above described embodiment, method, and examples, but by all embodiments and methods within the scope and spirit of the invention as claimed. 
     In the foregoing specification, the invention has been described with reference to specific examples of embodiments of the invention. It will, however, be evident that various modifications and changes may be made therein without departing from the broader spirit and scope of the invention as set forth in the appended claims. 
     Moreover, the terms “front,” “back,” “top,” “bottom,” “over,” “under” and the like in the description and in the claims, if any, are used for descriptive purposes and not necessarily for describing permanent relative positions. It is understood that the terms so used are interchangeable under appropriate circumstances such that the embodiments of the invention described herein are, for example, capable of operation in other orientations than those illustrated or otherwise described herein. 
     Furthermore, the terms “assert” or “set” and “negate” (or “deassert” or “clear”) are used herein when referring to the rendering of a signal, status bit, or similar apparatus into its logically true or logically false state, respectively. If the logically true state is a logic level one, the logically false state is a logic level zero. And if the logically true state is a logic level zero, the logically false state is a logic level one. 
     Those skilled in the art will recognize that the boundaries between logic blocks are merely illustrative and that alternative embodiments may merge logic blocks or circuit elements or impose an alternate decomposition of functionality upon various logic blocks or circuit elements. Thus, it is to be understood that the architectures depicted herein are merely exemplary, and that in fact many other architectures may be implemented which achieve the same functionality. 
     Any arrangement of components to achieve the same functionality is effectively “associated” such that the desired functionality is achieved. Hence, any two components herein combined to achieve a particular functionality may be seen as “associated with” each other such that the desired functionality is achieved, irrespective of architectures or intermedial components. Likewise, any two components so associated can also be viewed as being “operably connected,” or “operably coupled,” to each other to achieve the desired functionality. 
     Furthermore, those skilled in the art will recognize that boundaries between the above described operations merely illustrative. The multiple operations may be combined into a single operation, a single operation may be distributed in additional operations and operations may be executed at least partially overlapping in time. Moreover, alternative embodiments may include multiple instances of a particular operation, and the order of operations may be altered in various other embodiments. 
     Also for example, in one embodiment, the illustrated examples may be implemented as circuitry located on a single integrated circuit or within the same device. Alternatively, the examples may be implemented as any number of separate integrated circuits or separate devices interconnected with each other in a suitable manner. 
     However, other modifications, variations and alternatives are also possible. The specifications and drawings are, accordingly, to be regarded in an illustrative rather than in a restrictive sense. 
     In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word ‘comprising’ does not exclude the presence of other elements or steps then those listed in a claim. Furthermore, the terms “a” or “an,” as used herein, are defined as one or more than one. Also, the use of introductory phrases such as “at least one” and “one or more” in the claims should not be construed to imply that the introduction of another claim element by the indefinite articles “a” or “an” limits any particular claim containing such introduced claim element to inventions containing only one such element, even when the same claim includes the introductory phrases “one or more” or “at least one” and indefinite articles such as “a” or “an.” The same holds true for the use of definite articles. Unless stated otherwise, terms such as “first” and “second” are used to arbitrarily distinguish between the elements such terms describe. Thus, these terms are not necessarily intended to indicate temporal or other prioritization of such elements. The mere fact that certain measures are recited in mutually different claims does not indicate that a combination of these measures cannot be used to advantage. 
     While certain features of the invention have been illustrated and described herein, many modifications, substitutions, changes, and equivalents will now occur to those of ordinary skill in the art. It is, therefore, to be understood that the appended claims are intended to cover all such modifications and changes as fall within the true spirit of the invention. 
     It is appreciated that various features of the embodiments of the disclosure which are, for clarity, described in the contexts of separate embodiments may also be provided in combination in a single embodiment. Conversely, various features of the embodiments of the disclosure which are, for brevity, described in the context of a single embodiment may also be provided separately or in any suitable sub-combination. 
     It will be appreciated by persons skilled in the art that the embodiments of the disclosure are not limited by what has been particularly shown and described hereinabove. Rather the scope of the embodiments of the disclosure is defined by the appended claims and equivalents thereof.