Patent Publication Number: US-2021192937-A1

Title: System and method for vehicle identification

Description:
BACKGROUND 
     Intelligent infrastructure systems, such as parking lots and toll booths, may gather data regarding usage, such as by tracking vehicles entering the area. These systems may include various types of sensors that are statically mounted near the system. 
     Infrastructure sensor systems may communicate with nearby vehicles. Vehicle to outside systems (V2X) communication, such as vehicle-to-vehicle (V2V) communication and vehicle-to-infrastructure (V2I) communication are-increasingly used as inputs to improve vehicle safety and convenience. Smart infrastructure systems may offer features by communicating with a nearby vehicle, such as reserving a parking spot or providing directions to an open parking spot. 
     SUMMARY 
     In one exemplary embodiment, a method of identifying a target vehicle in a system includes providing a sensor within a system. Motion data of a plurality of vehicles within the system is detected with the sensor. Broadcast motion data is received from a vehicle unit within the system. Which of the plurality of vehicles is a target vehicle is determined based on a comparison of the detected motion data and the broadcast motion data. 
     In a further embodiment of any of the above, the detected motion data and the broadcast motion data have the same parameters. 
     In a further embodiment of any of the above, the detected motion data and the broadcast motion data comprise a vehicle speed. 
     In a further embodiment of any of the above, the detected motion data and the broadcast motion data comprise a vehicle yaw rate. 
     In a further embodiment of any of the above, the detected motion data and the broadcast motion data comprise a vertical acceleration. 
     In a further embodiment of any of the above, the detected motion data and the broadcast motion data comprise at least two parameters. 
     In a further embodiment of any of the above, the detecting, receiving, and determining steps are performed by a computing module. The computing module is in communication with the sensor and the vehicle unit. 
     In a further embodiment of any of the above, the computing module is configured to send information to the target vehicle. 
     In a further embodiment of any of the above, the vehicle unit communicates with the computing module wirelessly. 
     In a further embodiment of any of the above, broadcast motion data is received from multiple vehicle units within the system. 
     In a further embodiment of any of the above, the vehicle unit is mounted within one of the plurality of vehicles. 
     In a further embodiment of any of the above, the vehicle unit is a mobile device located within one of the plurality of vehicles. 
     In a further embodiment of any of the above, a plurality of sensors is provided within the system. 
     In a further embodiment of any of the above, the system is a portion of a paid or restricted access area. 
     In another exemplary embodiment, a system for identifying a target vehicle within a system includes a sensor configured to detect motion data of a plurality of vehicles within a system. A vehicle unit is mounted on a vehicle. The vehicle unit is configured to track motion of the vehicle and broadcast the tracked motion data to the computing module. A computing module is in communication with the sensor and the vehicle unit. The sensor is configured to send the detected motion data to the computing module. The vehicle unit is configured to broadcast the tracked motion data to the computing module. The computing module identifies the vehicle as a target vehicle based on a comparison of the detected motion data and the broadcast motion data. 
     In a further embodiment of any of the above, the detected motion data and the broadcast motion data comprise at least one of a vehicle speed, a vehicle yaw rate, and a vertical acceleration. 
     In a further embodiment of any of the above, the detected motion data and the broadcast motion data comprise at least two parameters. 
     In a further embodiment of any of the above, the vehicle unit is configured to communicate with the computing module wirelessly. 
     In a further embodiment of any of the above, the computing module is configured to send information to the target vehicle via vehicle unit. 
     In a further embodiment of any of the above, the computing module is configured to receive broadcast motion data from multiple vehicle units. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The disclosure can be further understood by reference to the following detailed description when considered in connection with the accompanying drawings wherein: 
         FIG. 1  schematically illustrates an example smart parking lot system. 
         FIG. 2  schematically illustrates vehicle movement in the example smart parking lot system. 
         FIG. 3  illustrates example vehicle speed data. 
         FIG. 4  illustrates example yaw rate data. 
         FIG. 5  illustrates example vertical acceleration data. 
         FIG. 6  illustrates an example method of identifying a target vehicle. 
     
    
    
     DETAILED DESCRIPTION 
     The subject invention provides a system and method for identifying vehicles within a smart system, such as a parking system. A sensor within the parking system tracks the motion of vehicles within the system. A vehicle unit on the vehicle broadcasts motion of the vehicle. The system then determines which vehicle within the system is a target vehicle based on a comparison of the tracked motion data and the broadcast motion data. 
       FIG. 1  illustrates an example smart infrastructure system, such as a parking system  10 . The parking system  10  generally includes a plurality of parking spaces  12 . The parking spaces  12  may include empty spaces  18  and occupied spaces  16 , which are occupied by parked vehicles  14 . An aisle  24  extends between the parking spaces  12 . In some examples, some of the parking spaces  12  may be designated as handicapped spaces  20  or high priority spaces  22 . Although a parking system  10  is shown and described herein, it should be understood that the disclosed system and method may be used for other systems, such as toll systems, for example. 
     The system  10  generally includes a sensor  30  and a computing module  34 . The sensor  30  is in communication with the computing module  34 . The sensor  30  may communicate with the computing module  34  via communication hardware, or wirelessly. In other embodiments, the sensor  30  and computing module  34  may be integrated into a single unit. The system  10  may include multiple sensors  30  mounted in different locations within the system  10 , each of the sensors  30  in communication with the computing module  34 . 
     The sensor  30  detects and tracks objects, such as vehicles  26 , within the system  10 . The sensor  30  may be a camera, a radar sensor, a lidar sensor, an ultrasonic sensor, or light beam, for example. The sensor  30  detects motion of the vehicles  26 . The sensor  30  then sends detected motion data about the vehicles  26  to the computing module  34 . The sensor  30  may detect motion data such as speed, acceleration, yaw rate, and steering angle, for example. The sensor  30  may detect motion data about multiple vehicles  26  within the system  10  simultaneously. The vehicle  26  has a vehicle unit  28  that is in communication with the computing module  34 . The vehicle unit  28  detects motion data of the vehicle  26  from aboard the vehicle  26 . The vehicle unit  28  may be integrated into the vehicle  26 , or may be a smart device located within the vehicle  26 , such as a smart phone or tablet. The vehicle unit  28  communicates wirelessly with the computing module  34 . The computing module  34  compares the detected motion data from the sensor  30  and the broadcasted motion data from the vehicle unit  28  to pair a particular detected vehicle  26  with a particular subscriber. 
     The computing module  34  may be calibrated to have data regarding the physical features of the parking system  10 . For example, the computing module  34  may be calibrated to have information regarding parking spaces  12  and aisles  24 . The sensor  30  may communicate with the computing module  34  via communication hardware, or may communicate wirelessly. The system  10  may use one or more of the following connection classes, for example: WLAN connection, e.g. based on IEEE 802.11, ISM (Industrial, Scientific, Medical Band) connection, Bluetooth® connection, ZigBee connection, UWB (ultrawide band) connection, WiMax® (Worldwide Interoperability for Microwave Access) connection, infrared connection, mobile radio connection, and/or radar-based communication. 
     The system  10 , and in particular the computing module  34 , may include one or more controllers comprising a processor, memory, and one or more input and/or output (I/O) device interface(s) that are communicatively coupled via a local interface. The local interface can include, for example but not limited to, one or more buses and/or other wired or wireless connections. The local interface may have additional elements, which are omitted for simplicity, such as controllers, buffers (caches), drivers, repeaters, and receivers to enable communications. Further, the local interface may include address, control, and/or data connections to enable appropriate communications among the aforementioned components. 
     The computing module  34  may include a hardware device for executing software, particularly software stored in memory, such as an algorithm for comparing motion data. The computing module  34  may include a custom made or commercially available processor, a central processing unit (CPU), an auxiliary processor among several processors associated with the computing module  34 , a semiconductor based microprocessor (in the form of a microchip or chip set), or generally any device for executing software instructions. The memory can include any one or combination of volatile memory elements (e.g., random access memory (RAM, such as DRAM, SRAM, SDRAM, VRAM, etc.)) and/or nonvolatile memory elements (e.g., ROM, hard drive, tape, CD-ROM, etc.). Moreover, the memory may incorporate electronic, magnetic, optical, and/or other types of storage media. Note that the memory can also have a distributed architecture, where various components are situated remotely from one another, but can be accessed by the processor. 
     The software in the memory may include one or more separate programs, each of which includes an ordered listing of executable instructions for implementing logical functions. A system component embodied as software may also be construed as a source program, executable program (object code), script, or any other entity comprising a set of instructions to be performed. When constructed as a source program, the program is translated via a compiler, assembler, interpreter, or the like, which may or may not be included within the memory. 
     The controller can be configured to execute software stored within the memory, to communicate data to and from the memory, and to generally control operations of the computing module  34  pursuant to the software. Software in memory, in whole or in part, is read by the processor, perhaps buffered within the processor, and then executed. This software may be used to store and compare the motion data from the sensor  30  and vehicle unit  28 , for example. 
     Intelligent infrastructure services, such as parking and toll systems, are rapidly expanding along with the expansion of smart devices and internet enabled vehicles. Such systems may offer features such as synchronized directions to a reserved parking spot, premium versus economy parking in the same lot, hourly billing, exclusive travel lanes, and other features that require the system to pair a subscriber or user&#39;s account with the vehicle accepting the service. This allows proper access to be pushed to the vehicle, charges to be made, or in some cases, notifications sent to maintenance or towing staff and/or police. In high traffic areas such as a busy parking lot with several simultaneous subscribers, some infrastructure sensor systems do not have enough information to properly distinguish between vehicles, and are thus unable to ensure the correct users are getting the services they are allocated. Such systems may require the customer to report the service received, such as parking spot number, which disrupts the otherwise automated nature of the service. 
     The parking system  10  utilizes both detected motion data from the sensor  30  and motion data broadcast from the vehicle  26  to determine a unique match between the vehicles observed in the environment and the particular subscriber&#39;s vehicle. Smart vehicles or smart devices that are actively subscribed to the infrastructure-based service will broadcast their dynamic motion data. This dynamic motion data may be speed, acceleration, angular velocity or yaw rate, or steering angle, for example. The infrastructure system then distinguishes among vehicles by matching motion data histories. Once the infrastructure system has identified a particular vehicle, it can send detailed directions to the subscriber, such as directions to an open parking space. 
       FIG. 2  schematically illustrates an example parking system  10  with several vehicles  40 ,  42 ,  44  moving through the system  10 . In this example, there are three vehicles  40 ,  42 ,  44  detected by the sensor  30  that are moving within the system  10 . In this example, vehicles  40 ,  44  have vehicle units  28  that are broadcasting data to the computing module  34 . The vehicle  40  has connected with the system  10  at point  60 . Thus, the vehicle  40  did not connect to the system  10  until after it had already entered the parking lot. The vehicle  44  connected with the system at point  62 . Thus, the vehicle  44  connected to the system  10  before passing through an entrance  32  of the parking lot. Thus, there will be a longer time period of broadcast data for vehicle  44  than for vehicle  40 . Vehicle  42  is not broadcasting data to the computing module  34  via a vehicle unit  28 . 
     The computing module  34  gathers detected motion data for each of the vehicles  40 ,  42 ,  44  within the system  10 . Each vehicle  40 ,  42 ,  44  has a unique path  50 ,  54 ,  52 , respectively. Each path  50 ,  54 ,  52  will result in unique motion data for the respective vehicle. Motion for each vehicle  40 ,  42 ,  44  is detected by the sensor  30 , and motion is broadcast for each vehicle that is connected to the system  10 . The detected and broadcast data are then compared to identify particular vehicles. For example, the data may be compared to identify which vehicle is a target vehicle. 
       FIGS. 3-5  illustrate examples of detected motion data and broadcast motion data for vehicles within the system  10 .  FIG. 3  illustrates example speed  70  over time  72  data for several tracked and broadcasting vehicles. The speed over time  74 ,  76 ,  78  of tracked objects  1 ,  2 , and  3  is the detected speed of the vehicles  40 ,  42 ,  44  detected by the sensor  30 . The speed over time  75 ,  77  for broadcasting objects  1  and  2  are the broadcast speed of vehicles  40 ,  44  after they are connected to the system  10 . In some examples, the broadcast data may be over a shorter time if the broadcasting vehicle does not connect to the system  10  right away. The computing module  34  compares the data for tracked objects  1 ,  2 ,  3  with the data for broadcasting objects  1  and  2  to identify which of the tracked objects are which. Here, based on the speed data, the computing module  34  is able to determine that broadcasting object  1  corresponds to tracked object  3 , and broadcasting object  2  corresponds to tracked object  1 . 
       FIG. 4  illustrates example yaw rate  80  over time  72  data for several tracked and broadcasting vehicles. The yaw rate over time  84 ,  86 ,  88  of tracked objects  1 ,  2 , and  3  is the detected yaw rate of the vehicles  40 ,  42 ,  44  detected by the sensor  30 . The yaw rate over time  85 ,  87  for broadcasting objects  1  and  2  are the broadcast yaw rate of vehicles  40 ,  44  after they are connected to the system  10 . The computing module  34  compares the data for tracked objects  1 ,  2 ,  3  with the data for broadcasting objects  1  and  2  to identify which of the tracked objects are which. Here, based on the yaw rate data, the computing module  34  is able to determine that broadcasting object  1  corresponds to tracked object  3 , and broadcasting object  2  corresponds to tracked object  1 . 
       FIG. 5  illustrates example vertical acceleration  90  over time  72  data for several tracked and broadcasting vehicles. The vertical acceleration over time  94 ,  96 ,  98  of tracked objects  1 ,  2 , and  3  is the detected vertical acceleration of the vehicles  40 ,  42 ,  44  detected by the sensor  30 . The vertical acceleration over time  95 ,  97  for broadcasting objects  1  and  2  are the broadcast vertical acceleration of vehicles  40 ,  44  after they are connected to the system  10 . Here, the changes in vertical acceleration generally correspond to speed bumps  36  (shown in  FIG. 2 ) within the system  10 . The computing module  34  compares the data for tracked objects  1 ,  2 ,  3  with the data for broadcasting objects  1  and  2  to identify which of the tracked objects are which. Here, based on the vertical acceleration data, the computing module  34  is able to determine that broadcasting object  1  corresponds to tracked object  3 , and broadcasting object  2  corresponds to tracked object  1  or  2 . In this case, since the data  97  could correspond to the data  94  or  96 , additional parameters may be needed to help identify particular vehicles. The computing module  34  may also have stored data regarding the location of speed bumps  36  to help identify particular vehicles within the system  10  based on the vehicle location during changes in vertical acceleration. Although the illustrated example shows speed bumps  36  as the primary changes in vertical acceleration, other features of the system  10  may impact vertical acceleration, such as pot holes and hills. 
     In some of these examples, a single motion parameter is sufficient to identify a particular vehicle. In other examples, multiple motion parameters may be required to identify vehicles. For example, the computing module  34  may rely on any combination of the above described parameters, or additional parameters, such as steering angle. Although a parking system  10  is shown and described, it should be understood that the disclosed system and method may be used for other systems. The system may be any subscription based service for a region of a paid and/or restricted access area. The system may be a toll road, private drive, garage, or vehicle elevator, for example. 
       FIG. 6  summarizes an example method  100  of identifying vehicles within the parking system  10 . Motion of vehicles  26  within the system  10  are detected with a sensor  30  at  102 . The sensor  30  may detect the motion of all vehicles  28  within the system  10 . In some examples, multiple sensors  30  may be utilized to detect the motion of the vehicles  28  within the system  10 . This detected motion data is sent to a computing module  34 . The computing module  34  also gathers broadcast motion data from broadcasting vehicles in the system  10  at  104 . Broadcasting vehicles are those that send motion data from the vehicle itself to the computing system  34  via a vehicle unit  28 . The computing module  34  then compares the detected motion data with the broadcast motion data at  106 . Based on this comparison, the computing module  34  may identify a target vehicle at  108 . The target vehicle may be a subscriber to a parking or other smart system that is broadcasting motion data, for example. In some examples, the computing module  34  may then send information to the target vehicle at  110 . For example, the computing module  34  may send directions to a parking space, or other information useful to the subscriber. This method utilizes motion data from both a static sensor  30  within the system  10  and motion data from the vehicle itself broadcast via a vehicle unit  28  to identify vehicles within the system. 
     The disclosed system and method provides a way to identify vehicles within a parking lot or other system. Some known systems rely on a link to vehicle license plate or other identifying features of the vehicle. These known systems may intrude on a user&#39;s privacy by monitoring and tracking particular features of the user and/or user&#39;s vehicle. These systems may also require a link between a subscriber&#39;s account and a particular vehicle, which may be inconvenient for user&#39;s with multiple vehicles or driving a rental car. When the vehicle unit  28  is a mobile device, such as a smart phone, a subscriber can use a single account for multiple vehicles. The disclosed system may also be used to communicate with drivers of the vehicle about features in the parking lot. For example, handicapped or premium parking spaces may be reserved digitally, and communicated to subscribers through the vehicle unit  28 . In other words, during busy times, when a normally handicapped space is not being used, the system  10  may choose to make that space into a normal parking space to accommodate more vehicles in the parking lot. This information may be communicated to subscribers using vehicle units  28 , and improve efficiency of the parking system  10 . 
     It should also be understood that although a particular component arrangement is disclosed in the illustrated embodiment, other arrangements will benefit herefrom. Although particular step sequences are shown, described, and claimed, it should be understood that steps may be performed in any order, separated or combined unless otherwise indicated and will still benefit from the present invention. 
     Although the different examples have specific components shown in the illustrations, embodiments of this invention are not limited to those particular combinations. It is possible to use some of the components or features from one of the examples in combination with features or components from another one of the examples. 
     Although an example embodiment has been disclosed, a worker of ordinary skill in this art would recognize that certain modifications would come within the scope of the claims. For that reason, the following claims should be studied to determine their true scope and content.