Patent Publication Number: US-2023146051-A1

Title: Vehicle detection and identification through audio propagation

Description:
INTRODUCTION 
     The present disclosure relates to vehicle detection and identification. More specifically, the present disclosure relates to vehicle detection and identification through audio propagation. 
     As motor vehicles become more automated and the interface between a user and the vehicle increases, vehicles are increasingly expanding their roles beyond passenger transportation. For example, an auditory signaling mechanism emitted by the vehicle has the capability to transmit information during an emergency situation, as well as becoming an enabler of an interactive real-world gaming application. 
     While current vehicle identification systems achieve their intended purpose, there is a need for a new and improved system and method for vehicle detection and identification. 
     SUMMARY 
     According to several aspects, a method of motor vehicle detection and identification through audio propagation includes generating source autometric signatures; broadcasting autometric signatures; detecting the autometric signatures; visualizing the autometric signatures; determining known autometric signatures; and evaluating and sending autometric signatures to the motor vehicle. 
     In an additional aspect of the present disclosure, the method further includes determining the source of the autometric signature data. 
     In another aspect of the present disclosure, the motor vehicle communicates with an information exchange infrastructure. 
     In another aspect of the present disclosure, the method further includes detecting image similarities of the signatures with deep learning image comparison. 
     In another aspect of the present disclosure, the method further includes listening for autometric signals with a mobile device. 
     In another aspect of the present disclosure, the mobile device communicates with the information exchange infrastructure. 
     In another aspect of the present disclosure, generating autometric signatures includes character to frequency mapping. 
     In another aspect of the present disclosure generating autometric signatures includes character to temporal mapping. 
     In another aspect of the present disclosure, generating autometric signatures includes character to color mapping. 
     In another aspect of the present disclosure, visualizing the autometric signatures includes audio signature analysis and signal data visualization. 
     In another aspect of the present disclosure, the method further includes storing data associated with the autometric signatures in a source repository. 
     According to several aspects, a method of motor vehicle detection and identification through audio propagation includes generating source autometric signatures, including character to frequency mapping, character to temporal mapping, and character to color mapping; broadcasting autometric signatures and detecting the autometric signatures with a device; visualizing the autometric signatures, including audio signature analysis and signal data visualization; determining known autometric signatures; and evaluating and sending autometric signatures to the motor vehicle. 
     In another aspect of the present disclosure, the method further includes determining the source of the autometric signature data. 
     In another aspect of the present disclosure, the motor vehicle communicates with an information exchange infrastructure. 
     In another aspect of the present disclosure, the method further includes detecting image similarities of the signatures with deep learning image comparison. 
     In another aspect of the present disclosure, the method further includes listening for autometric signals with a mobile device. 
     In another aspect of the present disclosure, the mobile device communicates with the information exchange infrastructure. 
     In another aspect of the present disclosure, the method further includes storing data associated with the autometric signatures in a source repository. 
     According to several aspects, a method of motor vehicle detection and identification through audio propagation includes generating source autometric signatures, including character to frequency mapping, character to temporal mapping, and character to color mapping; visualizing the autometric signatures, including audio signature analysis and signal data visualization; determining known autometric signatures; detecting image similarities of the signatures with deep learning image comparison; and sending autometric signature to the motor vehicle. The motor vehicle communicates with an information exchange infrastructure and a mobile device listens to the autometric signals and communicates with the information exchange infrastructure. 
     In another aspect of the present disclosure, the method further comprises evaluating a sequence of chords to determine a distinct audio signature. 
     Further areas of applicability will become apparent from the description provided herein. It should be understood that the description and specific examples are intended for purposes of illustration only and are not intended to limit the scope of the present disclosure. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The drawings described herein are for illustration purposes only and are not intended to limit the scope of the present disclosure in any way. 
         FIG.  1    is a block diagram of a system for vehicle detection and identification according to an exemplary embodiment; 
         FIG.  2    is an image of an autometrics viability test with the system shown in  FIG.  1    according to an exemplary embodiment; 
         FIG.  3    is a drawing of data encoded frequency audio tones produced with the system shown in  FIG.  1    according to an exemplary embodiment; and 
         FIGS.  4  and  5    are drawings of data encoded rhythm audio tones produced with the system shown in  FIG.  1    according to an exemplary embodiment. 
     
    
    
     DETAILED DESCRIPTION 
     The following description is merely exemplary in nature and is not intended to limit the present disclosure, application, or uses. 
     Referring to  FIG.  1   , there is shown a system  10  that enables a vehicle  12  to transmit a uniquely identifiable audio signal  16  to communicate data as a gamification enabler or in situations where the vehicle  12  is not easily within reach. A detection apparatus, such as, for example, a mobile device  18  leverages the microphone capability of the mobile device  18  to listen and communicate with a back-office. 
     The back-office  20  provides an analytic process for audio autometrics. The back-office  20  includes a source autometrics signature generation module  28 , a data visualization module  26 , a storage module  36 , an image similarity detection module  24 , and an evaluation and send module  22 . The mobile device  18  communicates directly with the back-office  20 , as well as with the vehicle  12  and the back-office  20  through an information exchange infrastructure  14 . 
     The autometrics signature generation module  28  includes a set of sub-modules  50 ,  52  and  54 . The sub-module  50  encodes source data employing algorithmic mapping of audio frequencies, character and character position; the sub-module  52  determines the simultaneous tone timing and duration; and the sub-module  54  encodes an audio file and stores it in a known autometrics signature container. Information from the autometrics signature generation module  28  is transmitted to the data visualization module  26  and the storage module  36 . 
     The data visualization module  26  includes a set of sub-modules  40 ,  42  and  46 . The sub-module  40  filters, analyzes and represents autometrics signal data that is biased for frequency and biased for temporal signatures; the sub-module  42  generates vehicle audio signature depiction of the autometrics as sample data  44 ; and with the sample data  44 , the sub-module  46  produces a colorized image  48  by removing unused frequencies and mapping the leveraged frequencies to a predefined RGB color map.  FIG.  2    illustrates the amplitude and duration of the detection of  16  separate tones. Further details of the analytic process associated with the back-office  20  are described below. 
     The system  10  encodes and stores vehicle, gamer and exclusive attributes (metadata) leveraging a combination of audio and color to create a vehicle/object specific audio signature message and corresponding encoded message image representation. The system  10  creates the equivalent of a musical chord (simultaneously played tones) for each piece of data utilizing a selected frequency range, as shown in  FIG.  3   . Each chord provides an object ID of a vehicle, the type of the vehicle, and the trim level of the vehicle. 
     The execution of chords in sequence is enhanced by purposeful pauses (silences) between chord broadcasts. These pauses are applied in a chord-silence alternating sequence. This sequence establishes a rhythm (chord and silence durations, as shown in  FIG.  4   ). Rhythm is applied utilizing a mathematic function. For example, given an initial duration minimum value of x (in milliseconds), a duration separation value of y (in milliseconds), and a character position z (position of the character in the character set), the expression d (duration) = x + (y * z) - y. The unique (vehicle or object specific) sequence of chords and silences is also represented as a synesthetic-like image depiction that conveys the frequency and temporal aspects of the encoded message. Hence, the autometric is the unique combination of frequency chords and associated rhythm utilized to convey data. 
     Referring back to  FIG.  1   , the source autometric signature generation module  28  provides character to frequency mapping, and character to temporal mapping. For character to frequency mapping, the first and last chords in each transmission are pre-defined sets of sync tones outside the range of the character mapping. Content strings are encoded into chords, giving each a distinct audio signature within a defined character set. Each character of a content string is represented by a frequency value (Hz) employing the following expression: Hz=(hz_minimum+((position_in_string*character set length*hz_delta)+ ((character number+1)*hz_delta))-hz_delta). 
     For character to temporal mapping, the duration of each chord and the intervening silences are derived with the expression: ms=ms_minimum+(position*ms_delta) - ms_delta. And for character to color mapping, a hue, saturation, and luminosity (HSL) color value is assigned to each character based on the character and its position in its parent content string. Data  38  from the source autometrics signature generation module  28  is transmitted to the data visualization module  26 . Data from the source autometric signature generation module  28  is also transmitted to the storage module  36  as known autometric signatures. 
     The data visualization module  26  encodes the data  38 . Specifically, the data visualization module  26  provides audio signature analysis and signal visualization, starting with an audio file  38 . The signal data visualization provides a spectrogram image, which is the data processed to generate black and white spectrogram image representing the complete time domain and frequency domain, and colorized image depiction, which is data that is processed to generate a compressed, colorized image. 
     Data from the data visualization module  26  is transmitted to a decision module  34 , which determines the source of the audio data. If the source is from the autometrics signature generation module  28 , the data from the data visualization module is transmitted to the storage module  36 . Vehicle signature messages, vehicle attributes, vehicle black and white spectrogram and vehicle colorized spectrogram are sent to a distributed data mining repository. Image files and audio files are distributed (shared) across the data mining repository with capability to increase the footprint to reduce data density per physical resource as data volume increases. 
     Next the audio file is sent to the evaluation and send module  22 . Here, audio signature messages are sent as over-the-air (OTA) file push to the vehicle audio internal module for broadcast. Further, sound broadcast conditions are enabled via vehicle internal module triggers. For example, business use cases may require interaction from the vehicle operator to be enabled. As such, the vehicle  12  plays the audio signature message leveraging the infotainment or external speaker systems. 
     Next, the mobile device  18  provides detection and interaction with the cloud. Through the mobile device  18 , indicators are sent to the user to confirm that the microphone is active and is recording and to initiate audio recording. Feedback to the user is provided for both successful detection and failed engagements (via SMS/MMS/Push) by a two-factor interaction model. 
     Further, the mobile device  18  sends information OTA to the back office  20 , in particular, the data visualization module  26 . In various arrangements, this information can contain encapsulated applicable metadata and audio files, including, such as, for example, gamification sending account, gamification destination account, emergency sending account, and emergency destination account. 
     If the decision module  34  determines that the source of the data from the data visualization module  26  originated from the mobile device  18 , the data is transmitted to the image similarity detection module  24  with deep learning image comparison of a set of images  30  to execute an analytic match  32 . 
     The module  24  analyzes RGB content of receiver compressed colorized images to identify the sync chord and end chord to align the message start and end points. The module  24  also makes a pixel-by-pixel comparison against the compressed colorized images in the storage module  36 . The output of the module  24  is a pixel match distribution percentage, and the match success is based on a tolerance criteria. 
     As stated above, the module  24  also provides high level Al deep learning. Accordingly, the data set and known outcomes are utilized to generate a decision model that is applied to fresh data to make judgements. Dependent applications also utilize the model to increase execution confidence. When new data is available, the model is re-trained and improved and the model is applied to fresh data to make judgements. In addition, the module  24  performs a population comparison size reduction utilizing image analysis. Hence, feature extraction is utilized to qualify an image as a member of a sub-population for deep learning comparisons. Sub-population approximations are performed by the deep learning system to identify confidence math level tolerances to address potential uncertainty derived from the broadcast attributes. Information is transmitted (SMS/MMS/Push) back to the mobile device  18  that an image match is found. Information may also be transmitted to other business systems, such as, for example, web service API. 
     The system  10  provides one or more of the following benefits: enables gaming activities played in an extensive outdoor area involving the collection of vehicle broadcasts to receive interactive feedback powered by a mobile application detection mechanism; provide communication of a vehicle’s metadata; provides an available detection apparatus with an application that leverages the microphone capability to listen and send captured audio and receive decoded signature messages and metadata; and provides an approach to detect broadcast metadata by law enforcement vehicles following a suspect vehicle within a physical distance range or emergency first responders identifying vehicles in distress. 
     The description of the present disclosure is merely exemplary in nature and variations that do not depart from the gist of the present disclosure are intended to be within the scope of the present disclosure. Such variations are not to be regarded as a departure from the spirit and scope of the present disclosure.