Patent Publication Number: US-2017374324-A1

Title: Vehicle with event recording

Description:
TECHNICAL FIELD 
     This disclosure relates to motor vehicles with sensors. 
     BACKGROUND 
     Vehicles include a range of sensors, which are capable of sensing data. A need exists to collect and organize this sensed data. 
     SUMMARY 
     A vehicle consistent with the disclosure includes: sensors, processor(s) configured to: make a primary detection; list objects located within a calculated focus area; mark the listed objects as partially identified or fully identified; estimate velocities of the partially identified objects; select connected vehicles based on the estimated velocities; instruct the connected vehicles to: record the partially identified objects, electronically deliver the recordings to an address. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       For a better understanding of the invention, reference may be made to embodiments shown in the following drawings. The components in the drawings are not necessarily to scale and related elements may be omitted, or in some instances proportions may have been exaggerated, so as to emphasize and clearly illustrate the novel features described herein. In addition, system components can be variously arranged, as known in the art. Further, in the drawings, like reference numerals designate corresponding parts throughout the several views. 
         FIG. 1  is a block diagram of a vehicle computing system. 
         FIG. 2  is a schematic of a vehicle including the vehicle computing system. 
         FIG. 3  is a top view of a town. 
         FIG. 4  illustrates a noise identification. 
         FIG. 5  is a block diagram of method corresponding to noise identification. 
         FIG. 6  is a top view of a home. 
         FIG. 7  is a block diagram of a first part of a method of identifying objects. 
         FIG. 8  is a block diagram of a second part of the method of identifying objects. 
         FIG. 9  is a top view of a vehicle and a virtual focus area. 
     
    
    
     DETAILED DESCRIPTION OF EXAMPLE EMBODIMENTS 
     While the invention may be embodied in various forms, there are shown in the drawings, and will hereinafter be described, some exemplary and non-limiting embodiments, with the understanding that the present disclosure is to be considered an exemplification of the invention and is not intended to limit the invention to the specific embodiments illustrated. 
     In this application, the use of the disjunctive is intended to include the conjunctive. The use of definite or indefinite articles is not intended to indicate cardinality. In particular, a reference to “the” object or “a” and “an” object is intended to denote also one of a possible plurality of such objects. Further, the conjunction “or” may be used to convey features that are simultaneously present, as one option, and mutually exclusive alternatives as another option. In other words, the conjunction “or” should be understood to include “and/or” as one option and “either/or” as another option. 
       FIG. 1  shows a computing system  100  of an example vehicle  200 . The vehicle  200  is also referred to as a first vehicle  200 . The vehicle  200  includes a motor, a battery, at least one wheel driven by the motor, and a steering system configured to turn the at least one wheel about an axis. Suitable vehicles are also described, for example, in U.S. patent application Ser. No. 14/991,496 to Miller et al. (“Miller”) and U.S. Pat. No. 8,180,547 to Prasad et al. (“Prasad”), both of which are hereby incorporated by reference in their entireties. The computing system  100  enables automatic control of mechanical systems within the device. It also enables communication with external devices. The computing system  100  includes a data bus  101 , one or more processors  108 , volatile memory  107 , non-volatile memory  106 , user interfaces  105 , a telematics unit  104 , actuators and motors  103 , and local sensors  102 . 
     The term “loaded vehicle,” when used in the claims, is hereby defined to mean: “a vehicle including: a motor, a plurality of wheels, a power source, and a steering system; wherein the motor transmits torque to at least one of the plurality of wheels, thereby driving the at least one of the plurality of wheels; wherein the power source supplies energy to the motor; and wherein the steering system is configured to steer at least one of the plurality of wheels.” The term “equipped electric vehicle,” when used in the claims, is hereby defined to mean “a vehicle including: a battery, a plurality of wheels, a motor, a steering system; wherein the motor transmits torque to at least one of the plurality of wheels, thereby driving the at least one of the plurality of wheels; wherein the battery is rechargeable and is configured to supply electric energy to the motor, thereby driving the motor; and wherein the steering system is configured to steer at least one of the plurality of wheels.” 
     The data bus  101  traffics electronic signals or data between the electronic components. The processor  108  performs operations on the electronic signals or data to produce modified electronic signals or data. The volatile memory  107  stores data for immediate recall by the processor  108 . The non-volatile memory  106  stores data for recall to the volatile memory  107  and/or the processor  108 . The non-volatile memory  106  includes a range of non-volatile memories including hard drives, SSDs, DVDs, Blu-Rays, etc. The user interface  105  includes displays, touch-screen displays, keyboards, buttons, and other devices that enable user interaction with the computing system. The telematics unit  104  enables both wired and wireless communication with external processors via Bluetooth, cellular data (e.g., 3G, LTE), USB, etc. The telematics unit  104  may be configured to broadcast signals at a certain frequency (e.g., one type of vehicle to vehicle transmission at 1 kHz or 200 kHz, depending on calculations described below). The actuators/motors  103  produce physical results. Examples of actuators/motors include fuel injectors, windshield wipers, brake light circuits, transmissions, airbags, haptic motors or engines etc. The local sensors  102  transmit digital readings or measurements to the processor  108 . Examples of suitable sensors include temperature sensors, rotation sensors, seatbelt sensors, speed sensors, cameras, lidar sensors, radar sensors, etc. It should be appreciated that the various connected components of  FIG. 1  may include separate or dedicated processors and memory. Further detail of the structure and operations of the computing system  100  is described, for example, in Miller and/or Prasad. 
       FIG. 2  generally shows and illustrates the vehicle  200 , which includes the computing system  100 . Although not shown, the vehicle  200  is in operative wireless communication with a nomadic device, such as a mobile phone. Some of the local sensors  102  are mounted on the exterior of the vehicle  200 . Local sensor  102   a  may be an ultrasonic sensor, a lidar sensor, a camera, a video camera, and/or a microphone, etc. Local sensor  102   a  may be configured to detect objects leading the vehicle  200  as indicated by leading sensing range  104   a.  Local sensor  102   b  may be an ultrasonic sensor, a lidar sensor, a camera, a video camera, and/or a microphone, etc. Local sensor  102   b  may be configured to detect objects trailing the vehicle  200  as indicated by leading sensing range  104   b.  Left sensor  102   c  and right sensor  102   d  may be configured to perform the same functions for the left and right sides of the vehicle  200 . The vehicle  200  includes a host of other sensors  102  located in the vehicle interior or on the vehicle exterior. These sensors may include any or all of the sensors disclosed in Prasad. 
     It should be appreciated that the vehicle  200  is configured to perform the methods and operations described below. In some cases, the vehicle  200  is configured to perform these functions via computer programs stored on the volatile and/or non-volatile memories of the computing system  100 . A processor is “configured to” perform a disclosed operation when the processor is in operative communication with memory storing a software program with code or instructions embodying the disclosed operation. Further description of how the processor, memories, and programs cooperate appears in Prasad. It should be appreciated that the nomadic device or an external server in operative communication with the vehicle  200  perform some or all of the methods and operations discussed below. 
     According to various embodiments, the vehicle  200  is the vehicle  100   a  of Prasad. In various embodiments, the computing system  100  is the VCCS  102  of  FIG. 2  of Prasad. In various embodiments, the vehicle  200  is in communication with some or all of the devices shown in  FIG. 1  of Prasad, including the nomadic device  110 , the communication tower  116 , the telecom network  118 , the Internet  120 , and the data processing center  122 . 
       FIG. 3  generally shows and illustrates a town  300  including north/south roads  301   a,    301   b,    301   c,  east/west roads  302   a,    302   b,    302   c,  and a parking lot  304 . The roads  301 ,  302  intersect at nodes (i.e., intersections)  303   a,    303   b,    303   c,    303   d,    303   e,    303   f,    303   g,    303   g  and  303   i.  The vehicle  200  is configured to detect an event (e.g., a break-in or a hit-and-run), and then initiate or coordinate a search based on the detection.  FIGS. 7 and 8 , which are discussed in detail below, generally show and illustrate a method  700  for performing such a search.  FIG. 8  generally shows and illustrates additional details of block  716  of the method  700  of  FIG. 7 . 
     With reference to  FIG. 3 , the vehicle  200  is stopped in the parking lot  304 . The vehicle  200  detects an event such as the break-in or the hit-and-run. The vehicle  200  detects such an event via the local vehicle sensors  102 . For example, accelerometers may detect a sudden acceleration of the vehicle consistent with an impact; sensors connected to the vehicle doors and/or windows may detect a breakage of a window or an unauthorized opening of a door. This kind of detection is referred to as a primary detection and is generally identified via first local vehicle sensors that perpetually run when the vehicle  200  is parked and/or off. The vehicle  200  may be configured to accept a user-input via the user interface  105  commanding the vehicle  200  to make the primary detection. 
     With reference to  FIG. 7 , the vehicle  200  periodically polls the first local sensors at block  702 . The vehicle further evaluates the polls at block  702  by comparing the content of the polls to predetermined values. When one or more of the polls exceeds an associated predetermined value, the vehicle confirms a primary detection at block  704 . 
     Once the primary detection occurs at block  704 , the vehicle  200  is configured to apply information extracted from second local vehicle sensors to generate a composite of the event. Many people and/or vehicles may surround the vehicle  200 . Therefore, according to various embodiments, the vehicle  200  estimates an original time of the event, then tracks people and/or vehicles within a radius of the vehicle  200 , the radius being based on (a) the original time of the event and (b) time elapsed since the original time. 
     Additionally, according to various embodiments, the vehicle  200  identifies a side of the vehicle  200  associated with the event via the first local vehicle sensors. If, for example, an acceleration sensor on the left side of the vehicle  200  measured acceleration prior to the right side of the vehicle, then the vehicle  200  may assume that the event originated on the left side of the vehicle  200 . If a window is broken, then the vehicle  200  may identify the location of the broken window and then focus on the side corresponding to the broken window. 
     With reference to  FIG. 9 , according to various embodiments, the vehicle  200  combines the radius with the identified side to select a portion of the circular area defined by the radius. As shown in  FIG. 9 , the vehicle  200  has determined a radius  903  based on (a) the original time of the event and (b) the time elapsed since the original time, and defined a circle  900  given the radius. As shown in  FIG. 9 , the vehicle  200  has determined that the event originated on the left side of the vehicle. The vehicle thus discards portion  902  of the circle  900  and sets portion  901   a  of the circle  900  as the focus area. Portion  901   a  of the circle  900  includes boundaries  901   b,    901   c,  and  901   d.  Boundaries  901   b  and  901   c  may be radial. Boundary  901   d  may track the surface of the left side of the vehicle. It should thus be appreciated that the focus area may resemble a trapezoid with a curved base. If a side cannot be identified, then the entire circle  900  defined by the radius  903  is the focus area. 
     Returning to block  708  of  FIG. 7 , the vehicle  200  counts each person and external vehicle (collectively referred to as “objects”) within the focus area. More specifically, the vehicle  200  builds an active tracking list and assigns a unique code to each object on the tracking list. The unique code organizes information contributed from multiple sources. Block  708  is further explained below. 
     With reference to block  708 , build the active tracking list, the vehicle  200  scans the surroundings with second local vehicle sensors. The second local vehicle sensors may be cameras. According to various embodiments, the second local vehicle sensors automatically turn off or deactivate when the vehicle is parked and/or turned off and are thus reactivated by the vehicle  200  at block  708 . 
     With reference to block  708 , the vehicle  200  applies known image filtering software to identify people and external vehicles (collectively “objects”) within the focus area. The vehicle  200  identifies external vehicles by their make, model, color, and/or license plate. The vehicle  200  identifies people with facial recognition technology, and/or technology that applies image recognition software to approximate, height, weight, skin-tone, hair color, etc. 
     With reference to block  708 , each identified vehicle or person is assigned a separate entry in the active tracking list. After block  708 , the vehicle  200  has generated an active tracking list that has, for each counted object in the focus area: a unique and randomly generated ID, a type of the object (e.g., vehicle or person), and detected characteristics of the object (e.g., make, model, hair color, eye color, height, etc.). 
     At block  710 , the vehicle  200  reviews the information (i.e., the detected characteristics) associated with each object and assigns a confidence to an identity of the object based on the reviewed information. The confidence is based on a quality of the identification. For external vehicles, the vehicle  200  may assign a full confidence only when it has captured a suitable (e.g., non-blurred) image of the license plate such that the vehicle  200  can read (via OCR technology) each individual character of the license plate. For people, the vehicle  200  may assign a full confidence only when a predetermined level of facial recognition has been achieved. 
     The vehicle  200  thus, at block  710 , marks each object in the active tracking list as having a full confidence identity (i.e., being fully identified) or a partial or incomplete confidence identity (i.e., being partially identified). When an object has been identified with full confidence, the vehicle  200  no longer tracks the object. Accordingly, in block  712 , the vehicle  200  stores the identity of the object and removes the object from the active tracking list. When an object has not been identified with full confidence, the vehicle  200  is configured to collect additional information on the object. 
     The method  700  proceeds to block  714  when the vehicle  200  has partial or incomplete confidence in one of the identities. At block  714 , the vehicle  200  assigns a velocity (which includes a speed and heading) to the object. The vehicle  200  performs block  714  in anticipation of the object departing from the sensing range of the local sensors  102 . At block  716 , the vehicle  200  hands-off tracking of the object to other connected vehicles. According to various embodiments, the vehicle  200  perpetually cycles steps  708 ,  710 , and  714  for a partially identified object until the object is (a) identified with full confidence (i.e., fully identified), or (b) has departed from the sensing range of the local vehicle sensors  102  (i.e., until the local sensors  102  of the vehicle  200  can no longer resolve the object). 
       FIG. 8  generally shows and illustrates the handing-off process  716 . The vehicle  200  accesses a street map at block  802 , a map showing current locations of connected vehicles (i.e., vehicles configured to contribute tracking information) at block  804 , and the velocity and heading information for each partially identified object at block  806 . The maps of blocks  802  and  804  may be the same map. At block  808 , the vehicle  200  pairs or associates each partially identified object with at least one connected vehicle based on the information accessed in blocks  802 ,  804 , and  806 . 
     More specifically, and with reference to  FIG. 3 , the vehicle  200  builds, for each partially identified object, a supplementary search zone  305 .  FIG. 3  includes four example supplementary search zones  305   a,    305   b,    305   c,  and  305   d.  The vehicle  200  builds each supplementary search zone  305  based on the street map, the map of connected vehicles, and velocity and heading of each partially identified object. 
     More specifically, the vehicle  200  assesses the velocity and heading information for each partially identified object and, based on the velocity and heading, predicts the next node that the object will enter. For example, a partially identified object may have been last observed heading toward node  303   h  from parking lot  304 . The vehicle  200  generates a time window that the object will arrive at the predicted node (e.g., node  303   h ). The vehicle  200 , with reference to the map of connected vehicles, finds connected vehicles  200  expected to simultaneously occupy the node (e.g., node  303   h ) during the time window. 
     If no connected vehicles are projected to simultaneously occupy the predicted node with the object, then the vehicle  200  expands the supplementary search zone to encompass nodes adjacent to the predicted node. For example, if the supplementary search zone  305   d  initially only encompassed node  303   h,  then it could be expanded to encompass nodes  303   g  and  303   i,  as shown in  FIG. 3 . The vehicle  200  recruits connected vehicles for each node within the expanded search zone by repeating the above-described processes. According to various embodiments, newly encompassed nodes may be selected with a formula that assumes the partially identified object will not turn around (i.e., the expanded search zone  305   d  would not cover node  303   e ). 
     Returning to  FIG. 8 , the selected connected vehicles search for each partially identified object at block  810 . The selected connected vehicles search for objects matching the description existing in the active tracking list. If connected vehicles locate an object matching the existing description, then the connected vehicles supplement the active tracking list with newly recorded information at block  812 . 
     The vehicle  200  reviews the supplementary information and determines whether the object has been fully identified. If the supplementary information has resulted in a full identification, then the vehicle  200  removes the object from the active tracking list at block  814 . If the supplementary information has not resulted in a full confidence identification, then the vehicle  200  determines velocity and heading of the partially identified object based on information supplied by the connected vehicles at block  816   a  and hands-off tracking of the partially identified object at block  816   b.  A hand-off at block  816   b  causes the vehicle  200  to repeat the process of  FIG. 8 . 
     If the partially identified object was not found in the supplementary search zone, then the method proceeds to  818  where the vehicle  200  pairs the partially identified object with new connected vehicles by returning to block  808 . As previously discussed, when the vehicle  200  returns to block  808 , the vehicle  200  expands the supplementary search zone to encompass additional nodes. 
     It should be appreciated that although the above steps have been described as being coordinated by the vehicle  200 , some or all of the steps may be coordinated by a different computer, such as an external server in communication with the vehicle  200 . More specifically, a centralized server may be configured to perform or coordinate some or all of the steps. The vehicle  200  and the connected vehicles may be in operative communication with the centralized server and supply the centralized server with sensor readings, etc. 
       FIG. 4  generally shows and illustrates a use case of a noise identification strategy that can be performed by the vehicle  200 . The vehicle  200  may be configured to perform the noise identification strategy in addition to the methods of  FIGS. 7 and 8 . The vehicle  200  applies the noise identification strategy to identify an origin of a unique noise, such as a gunshot. In  FIG. 4 , local sensors  102   a  and  102   b  include microphones configured to record sound. 
     The vehicle  200  performs the noise identification strategy. Each of the local sensors  102   a  and  102   b  transmit signals representative of recorded sound to the computing system  100 . The computing system  100  identifies discrete noises within the recorded sound. The computing system  100  may perform such an identification, for example, with a Fourier transform that deconstructs sounds into constituent frequencies. Sound may be separated into discrete noises based on the constituent frequencies of the sound (e.g., sound with a high frequencies is a first noise, whereas sound with low frequencies is a second noise). 
     The identification may take into account a volume of the sound or amplitude of the frequencies when separating the sound into the discrete noises. It should be appreciated that a volume of a sound or noise is based on amplitude of the constituent frequencies of the sound or noise. It should thus be appreciated that when this disclosure refers to volume, the disclosure also refers to amplitudes of the constituent frequencies. 
     The computing system  100  matches discrete noises recorded at local sensor  102   a  with discrete noises recorded at local sensor  102   b.  More specifically, because local sensor  102   a  is spaced apart from local sensor  102   b,  noises will arrive at one of the local sensors first and another of the local sensors later. According to various embodiments, the computing system  100  only matches discrete noises that satisfy predetermined criteria. The predetermined criteria may include one or more frequencies and one or more amplitudes or volumes (e.g., only noises with a frequency within a specific range and with a volume above a specific level are matched). According to various embodiments, the predetermined criteria are updated based on information received via the telematics  104 . The received information may include weather information including information about times and locations of lightning strikes. Thus, upon receiving information about a lightning strike, the computing system  100  may adjust the predetermined criteria to exclude noises with profiles (frequencies and/or amplitudes) associated with lightning strikes. 
     The computing system  100  classifies a matched discrete noise based on the constituent frequencies of the discrete noise. A gunshot, for example, will generate a discrete noise with unique constituent frequencies. According to various embodiments, based on the classification, the computing system  100  estimates an origination volume of the noise. A gunshot, for example, may have produce sound with an original volume of 163 to 166 dB. It should be appreciated that the computing system  100  may apply other methods to determine an origination volume of the noise. For example, the computing system  100  may include more than two microphones and estimate an origination volume of the sound based on (a) the known distances between the microphones, (b) the constituent frequencies, and (c) attenuation of the volume or amplitudes of the noise between the microphones. 
     The computing system  100  builds a circular virtual fence centered around each microphone based on (a) the estimated origination volume of the noise, (b) the measured volume of the noise, and (c) the constituent frequencies of the noise. Sound or noise frequencies attenuate in a medium, such as air, at known rates with distance. Thus, if the original amplitudes of the frequencies are known, the measured amplitudes of the frequencies are known, and the attenuation rate is known, the distance can be estimated. 
       FIG. 4  shows a first virtual fence  401   a  centered around local sensor  102   a  and a second virtual fence  401   b  centered around local sensor  102   b.  First virtual fence  401   a  has a first radius  402   a.  Second virtual fence  401   b  has a second radius  102   b.  In this example, local sensor  102   a  recorded noise with a greater volume (i.e., amplitudes) than local sensor  102   b.  Thus, local sensor  102   a  is closer to the source of the noise than local sensor  102   b.  As a result, the first radius  402   a  is smaller than the second radius  402   b.    
     The computing system  100  determines intersections of the virtual fences. In  FIG. 4 , the first virtual fence  401   a  intersects the second virtual fence  401   b  at intersections  403  and  404 . It should be appreciated that additional microphones and additional virtual fences (e.g., a third virtual fence) may result in a single intersection. 
     The intersections  403  and  404  represent likely points of origination of the noise. The computing system  100  references the map of connected vehicles (see block  804  of  FIG. 8  and the related disclosure). The computing system  100  selects connected vehicles within a predetermined range of the likely points of origination. The computing system  100  instructs the selected vehicles to record, store, and/or upload images of their surroundings to a centralized database. The computing system  100  instructs the selected vehicles to append the recorded, stored, and/or uploaded images with a unique identifier. The centralized database collects images with the same unique identifier and saves the collected images in a specific location. A user, such as law enforcement, may download and view the collected images. 
       FIG. 5  generally shows and illustrates a method  500  of performing the use case identification strategy consistent with the above disclosure. According to various embodiments, the computing system  100  enables user suspension of some or all of these steps for a user-determined time span via the user interface  105 . Additionally, according to various embodiments, the computing system  100  is configured to receive a third-party command (e.g., from a remote user) directing the computing system to suspend some or all of these steps. Such a feature would enable law enforcement, for example, to avoid being inundated with a flood of detections. 
     At block  502 , the computing system  100  receives recorded sound from the local sensors  102  (i.e., the microphones). At block  504 , the computing system  100  segments or breaks the recorded sound into discrete noises. At block  506 , the computing system  100  compares features (e.g., frequencies and/or associated amplitudes) of each discrete noise to predetermined criteria (e.g., frequency and/or amplitude criteria). At block  508 , the computing system  100  matches a discrete noise recorded at one of the local sensors  102  with discrete noises recorded at the other local sensors  102 . According to various embodiments, the computing system  100  only proceeds to block  508  when a discrete noise of at least one of the local sensors  102  satisfies the predetermined criteria. 
     At block  510 , the computing system  100  estimates an origination volume of the noise according to some or all of the previously discussed methods. At block  512 , the computing system  100  builds the virtual fences (e.g., virtual fences  401   a  and  401   b ). At block  514 , the computing system  100  finds one or more intersections of the virtual fences (e.g., intersections  403  and  404 ). At block  516 , the computing system  100  references a map of connected vehicles and selects connected vehicles with a predetermined proximity of the intersections. At block  518 , the computing system  100  sends instructions to (i.e., recruits) the selected connected vehicles, such as the instructions to store, record, and/or upload images. It should be appreciated that an external server may perform some or all of the blocks of  FIG. 5  instead of the computing system  100 . 
     According to various embodiments, the computing system  100  or the external server performs the above process with respect to sounds matched between distinct connected vehicles. More specifically, the computing system  100  or the external server matches noise recorded at a local sensor of a first connected vehicle with noise recorded at a local sensor of a second connected vehicle. The external server or computing system  100  then performs similar method steps with reference to the known/measured/received distance between the distinct connected vehicles. In other words, the method functions according to the above steps when local sensor  102   a  is mounted on a first vehicle and local sensor  102   b  is mounted on a second vehicle. 
       FIG. 6  generally shows and illustrates a property  600  with a house  601 , a garage  602 , a front lawn  605 , and a driveway  603 . The driveway  603  joins a road  604 . The vehicle  200  is parked in the driveway. The property  600  is equipped with a home alarm or security system (not shown). When active, the security system is configured to detect opening of doors, windows, and/or the garage  602 . The security system performs such detections via known security technology. As is known in the art, the security system alerts a predetermined amount of time after a detection. Upon alerting, the security system broadcasts noises, activates lights, and/or broadcasts a distress call to a third party. 
     The security system is configured to communicate with the vehicle  200  via the telematics  104 . Upon detection and/or upon alerting, the security system, in addition to performing the above operations, instructs the vehicle  200  to (a) begin recording with the local vehicle sensors  102 , (b) activate a car alarm siren, (c) activate a horn, and/or (d) flash some or all of the lights. According to various embodiments, the vehicle  200  automatically uploads measurements or recordings of the local vehicle sensors to a centralized database and/or the third party. 
       FIG. 6  shows local sensor  102   a  capturing events within sensing range  104   a.  According to various embodiments, the security system is configured to receive and display the captured events on a screen located inside of the house  601 . According to various embodiments, the security system is configured to automatically and/or via user command, actuate the local sensor  102   a  to move or adjust the sensing range  104   a.  According to various embodiments, upon detection and/or upon alerting, the security system instructs the vehicle  200  to capture and upload 360 degree view around the vehicle  200  with the local sensors  102 . 
     The above disclosure references a map of connected vehicles. It should be appreciated that the map of connected vehicles may include static objects with suitable sensors (e.g., a camera perched on a traffic light). It should thus be appreciated that the above-described methods may include assigning particular tracking or identification tasks to the static objects in addition to the connected vehicles (i.e., the static objects are simply treated as connected vehicles with a velocity of zero).