Patent Publication Number: US-2020279489-A1

Title: Vehicle location detection

Description:
BACKGROUND 
     The present invention relates generally to a method, system, and computer program for vehicle location detection. More particularly, the present invention relates to a method, system, and computer program for determining the location of a parked vehicle using video and vehicle sensor analysis. 
     Locating a parked vehicle can be a frustrating and time-consuming task, especially if the parking area is big and complex. Further complicating the location of a parked vehicle is the amount of time that has passed since the vehicle was parked. For example, it can be hard to remember exactly where a vehicle is parked at a sports stadium, which can hold tens of thousands of cars, after attending a game for several hours. Currently, drivers need to manually record the parking location of their vehicle and remember how to get there. There are also ticketing systems currently in use in some locations which issue parking tickets/cards identifying the parking location. Further, there are camera systems that may be installed in and around a parking structure which capture the position of the parked vehicle and the license plate number and based on picture analysis can tell the driver where the vehicle is parked. 
     BRIEF SUMMARY 
     An embodiment of the invention may include a method, computer program product and computer system for vehicle location detection. The method, computer program product and computer system may include computing device which may receive image data from an imaging device associated with a vehicle and sensor data from a vehicle sensor device associated with the vehicle. The computing device may detect the vehicle has entered a parking scene based on the received image data. The computing device may detect the surroundings of the vehicle using the imaging device associated with the vehicle. The computing device may determine the vehicle is parking based on the received sensor data associated with the vehicle. The computing device may identify a parking location of the vehicle based on the received image data associated with the vehicle, receive image data associated with the identified parking location of the vehicle from an imaging device associated with a vehicle and detect the surroundings of the identified parking location. The computing device may generate a notification to a user associated with the vehicle, wherein the notification identifies the parking location of the vehicle. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1 a    illustrates a system for vehicle location detection, in accordance with an embodiment of the invention. 
         FIG. 1 b    illustrates example operating modules of the vehicle location detection program of  FIG. 1   a.    
         FIG. 2  is a flowchart illustrating an example method of the vehicle location detection in accordance with an embodiment of the invention. 
         FIG. 3  is a block diagram depicting the hardware components of the vehicle location detection system of  FIG. 1 , in accordance with an embodiment of the invention. 
         FIG. 4  illustrates a cloud computing environment, in accordance with an embodiment of the invention. 
         FIG. 5  illustrates a set of functional abstraction layers provided by the cloud computing environment of  FIG. 4 , in accordance with an embodiment of the invention. 
     
    
    
     DETAILED DESCRIPTION 
     Embodiments of the present invention will now be described in detail with reference to the accompanying Figures. 
     The following description with reference to the accompanying drawings is provided to assist in a comprehensive understanding of exemplary embodiments of the invention as defined by the claims and their equivalents. It includes various specific details to assist in that understanding but these are to be regarded as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the invention. In addition, descriptions of well-known functions and constructions may be omitted for clarity and conciseness. 
     The terms and words used in the following description and claims are not limited to the bibliographical meanings, but, are merely used to enable a clear and consistent understanding of the invention. Accordingly, it should be apparent to those skilled in the art that the following description of exemplary embodiments of the present invention is provided for illustration purpose only and not for the purpose of limiting the invention as defined by the appended claims and their equivalents. 
     It is to be understood that the singular forms “a,” “an,” and “the” include plural referents unless the context clearly dictates otherwise. Thus, for example, reference to “a component surface” includes reference to one or more of such surfaces unless the context clearly dictates otherwise. 
     Embodiments of the present invention provide a method, computer program, and computer system for determining the location of a parked vehicle using video analysis. More particularly, embodiments of the present invention utilize video captured from a vehicle to determine a parking location of the vehicle. Current technology does not utilize video captured from a vehicle to determine a parking location of the vehicle. Currently, existing systems utilize video captured from cameras separate from the vehicle, such as, parking lot cameras and street cameras. However, such camera systems have a prohibitively high costs as such systems need to cover the entire parking structure and every single individual parking space within the parking structure. Also, existing camera systems present privacy issues as the system may be accessible by anyone and thus one vehicle can be searched by anyone accessing the system. Accordingly, a need exists for alternative systems and methods for determining parking locations of vehicles and providing the parking location to a user. Embodiments of the present invention provide a means for utilizing vehicle sensor devices and imaging devices associated with a vehicle to detect a parking location of a vehicle and transmit that location to a user. 
     Reference will now be made in detail to the embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to like elements throughout. Embodiments of the invention are generally directed to a system for determining the location of a parked vehicle using video and vehicle sensor analysis. 
       FIG. 1  illustrates a vehicle location detection system  100 , in accordance with an embodiment of the invention. In an example embodiment, vehicle location detection system  100  includes an imaging device  110 , a vehicle sensor device  120 , server  130 , user device  140 , and vehicle  150 , interconnected via network  160 . 
     In the example embodiment, the network  160  is the Internet, representing a worldwide collection of networks and gateways to support communications between devices connected to the Internet. The network  160  may include, for example, wired, wireless or fiber optic connections. In other embodiments, the network  160  may be implemented as an intranet, a local area network (LAN), or a wide area network (WAN). In general, the network  160  can be any combination of connections and protocols that will support communications between the imaging device  110 , a vehicle sensor device  120 , server  130 , user device  140 , and vehicle  150 . 
     The imaging device  110  may include the image database  112 . The imaging device  110  may be any device capable of capturing the image data  114 . The image data  114  may include, but is not limited to, visual, audio, and/or textual data. For example, the imaging device  110  may capture video or images, or both, of the vehicle  150  and the surroundings of the vehicle  150 . The video and images captured by the imaging device  110  may also contain textual data, such as, but not limited to, road signs, signs, billboards, markings, etc. In the example embodiment, the imaging device  110  may be a camera, a computer, a tablet, a thin client, a cellphone, or any other device capable of capturing, storing, and/or compiling visual, audio, and/or textual data and sending that visual, audio, and/or textual data to and from other computing devices, such as the server  130 , the user device  140 , and the vehicle  150  via the network  160 . The imaging device  110  may be associated with the vehicle  150 . For example, the imaging device  110  may be, but not limited to, built in to the vehicle  150 , resident in the vehicle  150 , physically attached to vehicle  150 , and/or located within the vehicle  150 . Thus, the imaging device  110  is associated with the vehicle  150  and provides the image data  114  from the vehicle  150 . The imaging device  110  is described in more detail with reference to  FIG. 3 . 
     The image database  112  may store the image data  114 , i.e. the visual, audio, and/or textual data, captured by the imaging device  110 . The image database  112  may be any storage media capable of storing data capable of storing data, such as, but not limited to, storage media resident in the imaging device  110  and/or removeable storage media. For example, the image database  112  may be, but is not limited to, a hard drive, a solid stated drive, a USB drive, or a memory card, etc. The image database  112  is described in more detail above and with reference to  FIG. 3 . 
     The vehicle sensor device  120  may include the sensor database  122 . The vehicle sensor device  120  may be any device capable of capturing the sensor data  124 . The sensor data  124  may include, but is not limited to, vehicle speed, vehicle acceleration, vehicle braking, vehicle direction, vehicle gear, etc. For example, but not limited to, the vehicle sensor device  120  may detect the speed of the vehicle  150 , the direction the vehicle  150  is travelling, whether the vehicle  150  is stopped, and what gear the vehicle  150  is in, e.g. park, reverse, or drive, etc. In the example embodiment, the vehicle sensor device  120  may be an Internet of Things (IoT) device, a global positioning system (GPS) device, a radar system device, a light detection and ranging (LIDAR) system device, or any other device capable of capturing, storing, and/or compiling the sensor data  124  and sending the sensor data  124  to and from other computing devices, such as the server  130 , the user device  140 , and the vehicle  150  via the network  160 . The vehicle sensor device  120  may be, for example, but not limited to, built in to the vehicle  150 , resident in the vehicle  150 , physically attached to vehicle  150 , and/or located within the vehicle  150 . While only a single vehicle sensor device  120  is illustrated, the vehicle location detection system  100  may include one or more vehicle sensor devices. The vehicle sensor device  120  is described in more detail with reference to  FIG. 3 . 
     The sensor database  122  may store the sensor data  124 . The sensor database  122  may be any storage media capable of storing data capable of storing data, such as, but not limited to, storage media resident in the vehicle sensor device  120  and/or removeable storage media. For example, the sensor database  122  may be, but is not limited to, a hard drive, a solid stated drive, a 
     USB drive, or a memory card, etc. The sensor database  122  is described in more detail above and with reference to  FIG. 3 . 
     The server  130  may include the program database  132  and the vehicle location detection program  136 . In the example embodiment, the server  130  may be a desktop computer, a notebook, a laptop computer, a tablet computer, a thin client, or any other electronic device or computing system capable of storing compiling and organizing audio, visual, or textual content and receiving and sending that content to and from other computing devices, such as the imaging device  110 , the vehicle sensor device  120 , the user device  140 , and the vehicle  150  via network  160 . In some embodiments, the server  130  includes a collection of devices, or data sources, in order to collect the program data  134 . The server  130  is described in more detail with reference to  FIG. 3 . 
     The program database  132  may store the program data  134 . The program database  132  may be any storage media capable of storing data capable of storing data, such as, but not limited to, storage media resident in the server  130  and/or removeable storage media. For example, the program database  132  may be, but is not limited to, a hard drive, a solid stated drive, a USB drive, or a memory card, etc. The program database  132  is described in more detail above and with reference to  FIG. 3 . 
     The program data  134  may be a collection of audiovisual content including, but not limited to, audio, visual, and textual content. The program data  134  may be, for example, the image data  114  and the sensor data  124  received and/or collected from the imaging device  110  and the vehicle sensor device  120 . Further, the program data  134  may include user data such as, but not limited to, a user&#39;s identification, a user&#39;s phone number, a user&#39;s address, a user&#39;s preferences, e.g. contact preferences, and a list of the user device  140  associated with a user, etc. The program data  134  is located on the server  130  and can be accessed via the network  160 . In accordance with an embodiment of the invention, the program data  134  may be located on one or a plurality of servers  130 . 
     The vehicle location detection program  136  is a program capable of detecting when the vehicle  150  is parking, determining the parked location of the vehicle  150 , and sending that location to a user on the user device  140 . The vehicle location detection program  136  may receive the image data  114  and the sensor data  124 , which may be received and/or collected by the server  130  and stored as the program data  134  in the program database  132 . The vehicle location detection program  136  is described in more detail below with reference to  FIG. 1   b.    
     The user device  140  may include the user interface  142 . In the example embodiment, the user device  140  may be a cellphone, desktop computer, a notebook, a laptop computer, a tablet computer, a thin client, or any other electronic device or computing system capable of storing compiling and organizing audio, visual, or textual content and receiving and sending that content to and from other computing devices, such as the imaging device  110 , the vehicle sensor device  120 , the server  130 , and the vehicle  150  via the network  160 . While only a single user device  140  is depicted, it can be appreciated that any number of user devices may be part of the vehicle location detection system  100 . In some embodiments, the user device  140  includes a collection of devices or data sources. The user device  140  is described in more detail with reference to  FIG. 4 . 
     The user interface  142  includes components used to receive input from a user on the user device  140  and transmit the input to the vehicle location detection program  136  residing on server  130 , or conversely to receive information from the vehicle location detection program  136  and display the information to the user on user device  140 . In an example embodiment, the user interface  142  uses a combination of technologies and devices, such as device drivers, to provide a platform to enable users of the user device  140  to interact with the vehicle location detection program  136 . In the example embodiment, the user interface  142  receives input, such as but not limited to, textual, visual, or audio input received from a physical input device, such as but not limited to, a keypad and/or a microphone. 
     The vehicle  150  may be any vehicle including, but not limited to, motorized and non-motorized vehicles. The vehicle  150  may be, for example, but not limited to, a passenger car, a motorcycle, a commercial vehicle, a boat, a bicycle or any other vehicle capable of communicating with the imaging device  110 , vehicle sensor device  120 , the server  130 , and the user device  140  via the network  160 . In one embodiment of the invention the imaging device  110  and/or the vehicle sensor device  120  may be hardwired into the vehicle  150  and communicate with the vehicle  150  via the network  160 . In yet another embodiment of the invention, the imaging device  110  and/or the vehicle sensor device  120  may be separate devices which communicate with the vehicle  150  via the network  160 . Thus, the imaging device  110  and/or the vehicle sensor device  120  is associated with the vehicle  150  and provides data from the vehicle  150 . 
       FIG. 1 b    illustrates example modules of the vehicle location detection program  136 . In an example embodiment, the vehicle location detection program  136  may include five modules: scene detection module  170 , surroundings analysis module  172 , sensor analysis module  174 , location analysis module  176 , and notification module  178 . 
     The scene detection module  170  receives the image data  114  stored as the program data  134  from the program database  132 . The scene detection module  170  analyzes the image data  114  which is captured from the imaging device  110  to identify the type of location of the vehicle  150 . In an embodiment of the invention, the scene detection module  170  may analyze the image data  114  to determine if the vehicle  150  is in a parking location, e.g. a parking lot, a parking garage, street parking, etc., or on the road, e.g. actively driving. The scene detection module  170  may utilize visual recognition technology to determine the type of location of the vehicle  150 . For example, the visual recognition technology may be, but not limited to, a trained parking scene recognition model. The trained parking scene recognition model may be generated using neural networks, including, but not limited to, deep convolutional neural networks, and deep recurrent neural networks. Deep convolutional neural networks are a class of deep, feed-forward artificial neural networks consisting of an input layer, an output layer, and multiple hidden layers used to analyze images. Deep recurrent neural networks are artificial neural networks wherein the connections between the nodes of the network form a directed graph along a sequence used for analyzing linguistic data. The scene detection module  170  may input the received image data  114  into the convolutional neural networks to generate the trained parking scene recognition model. The trained parking scene recognition model determines if the vehicle  150  is in a parking scene or not. 
     The surroundings analysis module  172  receives the program data  134  from the program database  132 . The surroundings analysis module  172  analyzes the program data  134  to detect objects on the way to the parking position of the vehicle  150 , such as, for example, after entering the parking location. The surroundings analysis module  172  analyzes the program data  134  after the scene detection module  170  determines the vehicle  150  has entered a parking scene. For example, the surroundings analysis module  172  may analyze the image data  114  stored as program data  134  to detect objects on the way and the parking position of the vehicle  150  such as, but not limited to, advertisements, parking garage pillars, elevators, store fronts, lights, trees, or any unique object on the way and the parking position of the vehicle  150 . The surroundings analysis module  172  may utilize object recognition technology to detect objects on the way and the parking position of the vehicle  150 . For example, the object recognition technology may be, but not limited to, a trained object detection model. The trained object detection model may be generated using neural networks, including, but not limited to, deep convolutional neural networks, and deep recurrent neural networks. Deep convolutional neural networks are a class of deep, feed-forward artificial neural networks consisting of an input layer, an output layer, and multiple hidden layers used to analyze images. Deep recurrent neural networks are artificial neural networks wherein the connections between the nodes of the network form a directed graph along a sequence used for analyzing linguistic data. The surroundings analysis module  172  may input the program data  134  into the convolutional neural networks to generate the trained object detection model. The trained object detection model detects unique objects surrounding the parking location of the vehicle  150 . 
     The sensor analysis module  174  receives the sensor data  124  stored as the program data  134  from the program database  132 . The sensor analysis module  174  analyzes the sensor data  124  which is captured from the vehicle sensor device  120  to determine if the vehicle  150  is parking. For example, the sensor analysis module  174  may analyze the sensor data  124  to determine vehicle speed variation and identify vehicle events of the vehicle  150  such as, but not limited to, specific patterns for parking, e.g., speed reduced from normal driving speed, vehicle gear, e.g. reverse, drive, or park, and vehicle stoppage. 
     The location analysis module  176  receives the program data  134  from the program database  132 . The location analysis module  176  correlates the output of the scene detection module  170 , the surroundings analysis module  172 , and the sensor analysis module  174  to identify the parking location of the vehicle  150 . For example, the location analysis module  176  may correlate the detection of a parking scene by the scene detection module  170  with the objects detected by the surroundings analysis module  172  and the detection of a parking event by the sensor analysis module  174 . Further, the location analysis module  176  may receive the image data  114  stored as the program data  134  to further analyze the actual parking location of the vehicle  150 . For example, the location analysis module  176  may analyze any visual and/or textual data immediately surrounding the actual parking location of the vehicle  150  within the image data  114  such as, but not limited to, words and numbers, etc. The visual and/or textual data immediately surrounding the actual parking location of the vehicle  150  within the image data  114  may include, but is not limited to, a parking spot identifier, e.g. a space number, parking lot area identifier, a parking garage level, etc. The location analysis module  176  may utilize optical character recognition (OCR) to analyze the image data  114 . The location analysis module  176  may also utilize visual and/or object recognition technology to further analyze the actual parking location of the vehicle  150  as described above with reference to the scene detection module  170  and the surroundings analysis module  172 . In an embodiment of the invention, the location analysis module  176  may also collect video from the image data  114  of the parking location. 
     The notification module  178  generates a notification of the parking location of the vehicle  150  determined by the location analysis module  176  to a user on the user device  140  via the user interface  142 . The notification may include, but is not limited to, the determined parking location of the vehicle  150 , e.g. 2 nd  floor of parking garage space  223 , information of the surroundings of the determined parking location of the vehicle  150 , e.g. near the elevator, image data of the determined parking location of the vehicle  150 , e.g. a video or picture of the parking location captured by the imaging device  110 . The notification module  178  may send a notification to a user on the user device  140  based on user preferences which are stored in the program data  134  on the program database  132 . The user may enter user preferences using the user device  140  via the user interface  142 . User preferences may include, but are not limited to, a list of user devices associated with the user, frequency of notification, and what parking location information to include in a notification. While only a single user device  140  is depicted, the notification module  178  may send a notification to one or more user devices  140  depending on the user preferences. 
     Referring to  FIG. 2 , a method  200  for vehicle location detection is depicted, in accordance with an embodiment of the present invention. 
     Referring to block  210 , the vehicle location detection program  136  receives the image data  114  from the imaging device  110 . Image data retrieval is described in more detail above with reference to  FIG. 1   b.    
     Referring to block  212 , the vehicle location detection program  136  receives the sensor data  124  from the vehicle sensor device  120 . Sensor data retrieval is described in more detail above with reference to  FIG. 1   b.    
     Referring to block  214 , the scene detection module  170  detects the vehicle  150  has entered a parking scene based on the received the image data  114  stored as the program data  134  from the program database  132 . Parking scene detection is described in more detail above with reference to the scene detection module  170 . 
     Referring to block  216 , the surroundings analysis module  172  detects objects on the way to the parking position of the vehicle  150  based on the image data  114  stored as program data  134 . Surroundings analysis is described in more detail above with reference to the surroundings analysis module  172 . 
     Referring to block  218 , the sensor analysis module  174  determines if the vehicle  150  is parking based on the sensor data  124  which is captured from the vehicle sensor device  120  and stored as the program data  134 . Sensor data analysis is described in more detail above with reference to the sensor analysis module  174 . 
     Referring to block  220 , the location analysis module  176  identifies the parking location of the vehicle  150  by correlating the output of the scene detection module  170 , the surroundings analysis module  172 , and the sensor analysis module  174 . Parking location identification is described in more detail above with reference to the location analysis module  176 . 
     Referring to block  222 , the location analysis module  176  receives the image data  114  associated with the parking location of the vehicle  150  to further analyze the actual parking location of the vehicle  150 . Parking location image data retrieval is described in more detail above with reference to the location analysis module  176 . 
     Referring to block  224 , the location analysis module  176  detects the surroundings of the parking location of the vehicle  150  based on the received image data  114  associated the parking location of the vehicle  150 . Parking location surroundings detection is described in more detail above with reference to the location analysis module  176 . 
     Referring to block  226 , the notification module  178  generates a notification of the parking location of the vehicle  150  determined by the location analysis module  176  to a user on the user device  140  via the user interface  142 . Notification generation is described in more detail above with reference to the notification module  178 . 
     Referring to  FIG. 3 , a system  1000  includes a computer system or computer  1010  shown in the form of a generic computing device. The method  200  for example, may be embodied in a program(s)  1060  ( FIG. 3 ) embodied on a computer readable storage device, for example, generally referred to as memory  1030  and more specifically, computer readable storage medium  1050  as shown in  FIG. 3 . For example, memory  1030  can include storage media  1034  such as RAM (Random Access Memory) or ROM (Read Only Memory), and cache memory  1038 . The program  1060  is executable by the processing unit or processor  1020  of the computer system  1010  (to execute program steps, code, or program code). Additional data storage may also be embodied as a database  1110  which can include data  1114 . The computer system  1010  and the program  1060  shown in  FIG. 3  are generic representations of a computer and program that may be local to a user, or provided as a remote service (for example, as a cloud based service), and may be provided in further examples, using a website accessible using the communications network  1200  (e.g., interacting with a network, the Internet, or cloud services). It is understood that the computer system  1010  also generically represents herein a computer device or a computer included in a device, such as a laptop or desktop computer, etc., or one or more servers, alone or as part of a datacenter. The computer system can include a network adapter/interface  1026 , and an input/output (I/O) interface(s)  1022 . The I/O interface  1022  allows for input and output of data with an external device  1074  that may be connected to the computer system. The network adapter/interface  1026  may provide communications between the computer system a network generically shown as the communications network  1200 . 
     The computer  1010  may be described in the general context of computer system-executable instructions, such as program modules, being executed by a computer system. Generally, program modules may include routines, programs, objects, components, logic, data structures, and so on that perform particular tasks or implement particular abstract data types. The method steps and system components and techniques may be embodied in modules of the program  1060  for performing the tasks of each of the steps of the method and system. The modules are generically represented in  FIG. 3  as program modules  1064 . The program  1060  and program modules  1064  can execute specific steps, routines, sub-routines, instructions or code, of the program. 
     The method of the present disclosure can be run locally on a device such as a mobile device, or can be run a service, for instance, on the server  1100  which may be remote and can be accessed using the communications network  1200 . The program or executable instructions may also be offered as a service by a provider. The computer  1010  may be practiced in a distributed cloud computing environment where tasks are performed by remote processing devices that are linked through a communications network  1200 . In a distributed cloud computing environment, program modules may be located in both local and remote computer system storage media including memory storage devices. 
     More specifically, as shown in  FIG. 3 , the system  1000  includes the computer system  1010  shown in the form of a general-purpose computing device with illustrative periphery devices. The components of the computer system  1010  may include, but are not limited to, one or more processors or processing units  1020 , a system memory  1030 , and a bus  1014  that couples various system components including system memory  1030  to processor  1020 . 
     The bus  1014  represents one or more of any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, and a processor or local bus using any of a variety of bus architectures. By way of example, and not limitation, such architectures include Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnects (PCI) bus. 
     The computer  1010  can include a variety of computer readable media. Such media may be any available media that is accessible by the computer  1010  (e.g., computer system, or server), and can include both volatile and non-volatile media, as well as, removable and non-removable media. Computer memory  1030  can include additional computer readable media  1034  in the form of volatile memory, such as random access memory (RAM), and/or cache memory  1038 . The computer  1010  may further include other removable/non-removable, volatile/non-volatile computer storage media, in one example, portable computer readable storage media  1072 . In one embodiment, the computer readable storage medium  1050  can be provided for reading from and writing to a non-removable, non-volatile magnetic media. The computer readable storage medium  1050  can be embodied, for example, as a hard drive. Additional memory and data storage can be provided, for example, as the storage system  1110  (e.g., a database) for storing data  1114  and communicating with the processing unit  1020 . The database can be stored on or be part of a server  1100 . Although not shown, a magnetic disk drive for reading from and writing to a removable, non-volatile magnetic disk (e.g., a “floppy disk”), and an optical disk drive for reading from or writing to a removable, non-volatile optical disk such as a CD-ROM, DVD-ROM or other optical media can be provided. In such instances, each can be connected to bus  1014  by one or more data media interfaces. As will be further depicted and described below, memory  1030  may include at least one program product which can include one or more program modules that are configured to carry out the functions of embodiments of the present invention. As such, the computing device in  FIG. 4  becomes specifically configured to implement mechanisms of the illustrative embodiments and specifically configured to perform the operations and generated the outputs of described herein for determining a route based on a user&#39;s preferred environmental experiences. 
     The methods  200  ( FIG. 2 ), for example, may be embodied in one or more computer programs, generically referred to as a program(s)  1060  and can be stored in memory  1030  in the computer readable storage medium  1050 . The program  1060  can include program modules  1064 . The program modules  1064  can generally carry out functions and/or methodologies of embodiments of the invention as described herein. For example, the program modules  1064  can include the modules  170 - 178  described above with reference to Figure lb. The one or more programs  1060  are stored in memory  1030  and are executable by the processing unit  1020 . By way of example, the memory  1030  may store an operating system  1052 , one or more application programs  1054 , other program modules, and program data on the computer readable storage medium  1050 . It is understood that the program  1060 , and the operating system  1052  and the application program(s)  1054  stored on the computer readable storage medium  1050  are similarly executable by the processing unit  1020 . 
     The computer  1010  may also communicate with one or more external devices  1074  such as a keyboard, a pointing device, a display  1080 , etc.; one or more devices that enable a user to interact with the computer  1010 ; and/or any devices (e.g., network card, modem, etc.) that enables the computer  1010  to communicate with one or more other computing devices. Such communication can occur via the Input/Output (I/O) interfaces  1022 . Still yet, the computer  1010  can communicate with one or more networks  1200  such as a local area network (LAN), a general wide area network (WAN), and/or a public network (e.g., the Internet) via network adapter/interface  1026 . As depicted, network adapter  1026  communicates with the other components of the computer  1010  via bus  1014 . It should be understood that although not shown, other hardware and/or software components could be used in conjunction with the computer  1010 . Examples, include, but are not limited to: microcode, device drivers  1024 , redundant processing units, external disk drive arrays, RAID systems, tape drives, and data archival storage systems, etc. 
     It is understood that a computer or a program running on the computer  1010  may communicate with a server, embodied as the server  1100 , via one or more communications networks, embodied as the communications network  1200 . The communications network  1200  may include transmission media and network links which include, for example, wireless, wired, or optical fiber, and routers, firewalls, switches, and gateway computers. The communications network may include connections, such as wire, wireless communication links, or fiber optic cables. A communications network may represent a worldwide collection of networks and gateways, such as the Internet, that use various protocols to communicate with one another, such as Lightweight Directory Access Protocol (LDAP), Transport Control Protocol/Internet Protocol (TCP/IP), Hypertext Transport Protocol (HTTP), Wireless Application Protocol (WAP), etc. A network may also include a number of different types of networks, such as, for example, an intranet, a local area network (LAN), or a wide area network (WAN). 
     In one example, a computer can use a network which may access a website on the Web (World Wide Web) using the Internet. In one embodiment, a computer  1010 , including a mobile device, can use a communications system or network  1200  which can include the Internet, or a public switched telephone network (PSTN) for example, a cellular network. The PSTN may include telephone lines, fiber optic cables, microwave transmission links, cellular networks, and communications satellites. The Internet may facilitate numerous searching and texting techniques, for example, using a cell phone or laptop computer to send queries to search engines via text messages (SMS), Multimedia Messaging Service (MMS) (related to SMS), email, or a web browser. The search engine can retrieve search results, that is, links to websites, documents, or other downloadable data that correspond to the query, and similarly, provide the search results to the user via the device as, for example, a web page of search results. 
     It is to be understood that although this disclosure includes a detailed description on cloud computing, implementation of the teachings recited herein are not limited to a cloud computing environment. Rather, embodiments of the present invention are capable of being implemented in conjunction with any other type of computing environment now known or later developed. 
     Cloud computing is a model of service delivery for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, network bandwidth, servers, processing, memory, storage, applications, virtual machines, and services) that can be rapidly provisioned and released with minimal management effort or interaction with a provider of the service. This cloud model may include at least five characteristics, at least three service models, and at least four deployment models. 
     Characteristics are as follows: 
     On-demand self-service: a cloud consumer can unilaterally provision computing capabilities, such as server time and network storage, as needed automatically without requiring human interaction with the service&#39;s provider. 
     Broad network access: capabilities are available over a network and accessed through standard mechanisms that promote use by heterogeneous thin or thick client platforms (e.g., mobile phones, laptops, and PDAs). 
     Resource pooling: the provider&#39;s computing resources are pooled to serve multiple consumers using a multi-tenant model, with different physical and virtual resources dynamically assigned and reassigned according to demand. There is a sense of location independence in that the consumer generally has no control or knowledge over the exact location of the provided resources but may be able to specify location at a higher level of abstraction (e.g., country, state, or datacenter). 
     Rapid elasticity: capabilities can be rapidly and elastically provisioned, in some cases automatically, to quickly scale out and rapidly released to quickly scale in. To the consumer, the capabilities available for provisioning often appear to be unlimited and can be purchased in any quantity at any time. 
     Measured service: cloud systems automatically control and optimize resource use by leveraging a metering capability at some level of abstraction appropriate to the type of service (e.g., storage, processing, bandwidth, and active user accounts). Resource usage can be monitored, controlled, and reported, providing transparency for both the provider and consumer of the utilized service. 
     Service Models are as follows: 
     Software as a Service (SaaS): the capability provided to the consumer is to use the provider&#39;s applications running on a cloud infrastructure. The applications are accessible from various client devices through a thin client interface such as a web browser (e.g., web-based e-mail). The consumer does not manage or control the underlying cloud infrastructure including network, servers, operating systems, storage, or even individual application capabilities, with the possible exception of limited user-specific application configuration settings. 
     Platform as a Service (PaaS): the capability provided to the consumer is to deploy onto the cloud infrastructure consumer-created or acquired applications created using programming languages and tools supported by the provider. The consumer does not manage or control the underlying cloud infrastructure including networks, servers, operating systems, or storage, but has control over the deployed applications and possibly application hosting environment configurations. 
     Infrastructure as a Service (IaaS): the capability provided to the consumer is to provision processing, storage, networks, and other fundamental computing resources where the consumer is able to deploy and run arbitrary software, which can include operating systems and applications. 
     The consumer does not manage or control the underlying cloud infrastructure but has control over operating systems, storage, deployed applications, and possibly limited control of select networking components (e.g., host firewalls). 
     Deployment Models are as follows: 
     Private cloud: the cloud infrastructure is operated solely for an organization. It may be managed by the organization or a third party and may exist on-premises or off-premises. 
     Community cloud: the cloud infrastructure is shared by several organizations and supports a specific community that has shared concerns (e.g., mission, security requirements, policy, and compliance considerations). It may be managed by the organizations or a third party and may exist on-premises or off-premises. 
     Public cloud: the cloud infrastructure is made available to the general public or a large industry group and is owned by an organization selling cloud services. 
     Hybrid cloud: the cloud infrastructure is a composition of two or more clouds (private, community, or public) that remain unique entities but are bound together by standardized or proprietary technology that enables data and application portability (e.g., cloud bursting for load-balancing between clouds). 
     A cloud computing environment is service oriented with a focus on statelessness, low coupling, modularity, and semantic interoperability. At the heart of cloud computing is an infrastructure that includes a network of interconnected nodes. 
     Referring now to  FIG. 4 , illustrative cloud computing environment  50  is depicted. As shown, cloud computing environment  50  includes one or more cloud computing nodes  10  with which local computing devices used by cloud consumers, such as, for example, personal digital assistant (PDA) or cellular telephone  54 A, desktop computer  54 B, laptop computer  54 C, and/or automobile computer system  54 N may communicate. Nodes  10  may communicate with one another. They may be grouped (not shown) physically or virtually, in one or more networks, such as Private, Community, Public, or Hybrid clouds as described hereinabove, or a combination thereof. This allows cloud computing environment  50  to offer infrastructure, platforms and/or software as services for which a cloud consumer does not need to maintain resources on a local computing device. It is understood that the types of computing devices  54 A-N shown in  FIG. 4  are intended to be illustrative only and that computing nodes  10  and cloud computing environment  50  can communicate with any type of computerized device over any type of network and/or network addressable connection (e.g., using a web browser). 
     Referring now to  FIG. 5 , a set of functional abstraction layers provided by cloud computing environment  50  ( FIG. 4 ) is shown. It should be understood in advance that the components, layers, and functions shown in  FIG. 5  are intended to be illustrative only and embodiments of the invention are not limited thereto. As depicted, the following layers and corresponding functions are provided: 
     Hardware and software layer  60  includes hardware and software components. Examples of hardware components include: mainframes  61 ; RISC (Reduced Instruction Set Computer) architecture based servers  62 ; servers  63 ; blade servers  64 ; storage devices  65 ; and networks and networking components  66 . In some embodiments, software components include network application server software  67  and database software  68 . 
     Virtualization layer  70  provides an abstraction layer from which the following examples of virtual entities may be provided: virtual servers  71 ; virtual storage  72 ; virtual networks  73 , including virtual private networks; virtual applications and operating systems  74 ; and virtual clients  75 . 
     In one example, management layer  80  may provide the functions described below. Resource provisioning  81  provides dynamic procurement of computing resources and other resources that are utilized to perform tasks within the cloud computing environment. Metering and Pricing  82  provide cost tracking as resources are utilized within the cloud computing environment, and billing or invoicing for consumption of these resources. In one example, these resources may include application software licenses. Security provides identity verification for cloud consumers and tasks, as well as protection for data and other resources. User portal  83  provides access to the cloud computing environment for consumers and system administrators. Service level management  84  provides cloud computing resource allocation and management such that required service levels are met. Service Level Agreement (SLA) planning and fulfillment  85  provide pre-arrangement for, and procurement of, cloud computing resources for which a future requirement is anticipated in accordance with an SLA. 
     Workloads layer  90  provides examples of functionality for which the cloud computing environment may be utilized. Examples of workloads and functions which may be provided from this layer include: mapping and navigation  91 ; software development and lifecycle management  92 ; virtual classroom education delivery  93 ; data analytics processing  94 ; transaction processing  95 ; and vehicle location detection  96 . 
     The programs described herein are identified based upon the application for which they are implemented in a specific embodiment of the invention. However, it should be appreciated that any particular program nomenclature herein is used merely for convenience, and thus the invention should not be limited to use solely in any specific application identified and/or implied by such nomenclature. 
     The present invention may be a system, a method, and/or a computer program product at any possible technical detail level of integration. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention. 
     The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire. 
     Computer readable program instructions described herein can be downloaded to respective computing/processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and/or a wireless network. The network may comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. A network adapter card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing/processing device. 
     Computer readable program instructions for carrying out operations of the present invention may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, configuration data for integrated circuitry, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++, or the like, and procedural programming languages, such as the “C” programming language or similar programming languages. The computer readable program instructions may execute entirely on the user&#39;s computer, partly on the user&#39;s computer, as a stand-alone software package, partly on the user&#39;s computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user&#39;s computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present invention. 
     Aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer readable program instructions. 
     These computer readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, a programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement aspects of the function/act specified in the flowchart and/or block diagram block or blocks. 
     The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions/acts specified in the flowchart and/or block diagram block or blocks. 
     The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the blocks may occur out of the order noted in the Figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions. 
     While steps of the disclosed method and components of the disclosed systems and environments have been sequentially or serially identified using numbers and letters, such numbering or lettering is not an indication that such steps must be performed in the order recited, and is merely provided to facilitate clear referencing of the method&#39;s steps. Furthermore, steps of the method may be performed in parallel to perform their described functionality.