Abstract:
An image of a vehicle user is captured. The image is compared to a corresponding account image in a preset profile. It is determined whether the image matches the corresponding account image. A destination is selected based on the user profile. A traffic report is generated based on the destination.

Description:
BACKGROUND 
     Vehicles typically include various settings that can be adjusted for a particular user. For example, a user may wish to adjust a vehicle seat, mirrors, etc., based on the user&#39;s size. When multiple users have access to a vehicle, each of the users may require or desire different settings. Improved mechanisms are needed for identifying users and/or for providing custom settings. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a block diagram of an exemplary system for implementing vehicle settings. 
         FIG. 2  is a flow diagram of an exemplary method for implementing vehicle settings. 
         FIG. 3  is a flow diagram of an exemplary method for a vehicle anti-theft measure. 
     
    
    
     DETAILED DESCRIPTION 
     Multiple users may use a vehicle  101 . Various vehicle  101  settings may be adjustable and/or individualized for each user. As disclosed herein, such settings, including a navigation system providing a preferred and/or individualized travel route, traffic information relating to such route, and the like, may be provided for a user entering a vehicle  101  based on a recognition of the user. For example, such recognition may be performed using data from a data collector  110  such as a camera in the vehicle  101  using facial recognition techniques. A profile for various users of a vehicle  101  may be stored in a data store in the vehicle  101 , e.g., a memory of the computer  105 . The profile may include various personalized settings, such as frequently traveled routes, preferred routes, preferred navigational options, times of travel, etc. Thus, once a user is recognized, traffic data relevant to a user&#39;s likely route of travel may be obtained and provided, and/or may be used to recommend a route to the user. 
       FIG. 1  illustrates a system  100  for providing customer navigational data based on performing facial recognition with respect to a vehicle  101  user. The vehicle  101  includes a computing device  105 , a data collector  110 , and a data store  115 , e.g., one or more memories or media included in or communicatively coupled to the computing device  105 . 
     The computing device  105  communicates over a network  120  with a remote computer server  125 , the server  125  typically including or being communicatively coupled to a network data store  130 . The network  120  may include one or more known networks and/or networking technologies, such as wireless communication networks (e.g., Bluetooth, IEEE 802.11, etc.), a cellular network, local area networks (LAN) and/or wide area networks (WAN), including the Internet, providing data communication services. 
     The data collector  110  collects identifying data of the user, e.g., an image. The data collector  110  may be any device suitable to collect identifying data, e.g. a camera. The data collector  110  is configured to send data to the computing device  105  and the data store  115 , e.g., each of the computing device  105 , the data collector  110 , in the data store  115  may be communicatively coupled to a vehicle  101  network, such as a controller area network (CAN) bus or the like. As described in more detail below, using data from the data collector  110 , the computer  105  is programmed to identify a user, and to retrieve user data from the data store  115 . User profile data from the data store  115   
     The data store  115 , the server  125 , and the network data store  130  may be of any suitable type, e.g., hard disk drives, solid-state drives, or any other volatile or non-volatile media. The data store  115  and the network data store  130  may store data sent over the network  120 . 
     One or more user devices  150  may be connected to the network  120 . User devices  150  may include commonly carried devices such as one or more of cellular telephones, e.g., a smart phone or the like, tablet devices, laptop computers, etc. 
     A remote site  160 , e.g. a third-party website or program, also connect to the network  120 . The computing device  105  may send and receive data to and from one or more remote sites  160 , e.g. traffic data, weather data, navigation, social media interactions, etc. 
       FIG. 2  illustrates a process  200  for implementing preset vehicle settings based on user facial recognition. The process  200  starts in a block  205 , where the data collector  110 , captures an image of a vehicle user. 
     Next, in a block  210 , the computing device  105  compares the image to images that have been stored in the data store  115  and included in respective user profiles to determine if the user is a pre-approved driver of the vehicle  101 . Images could be stored in the data store  115  and included in a user profile via a variety of mechanisms. For example, a user could perform a set-up process whereby the computer  105  executes instructions to capture an image of the user, and the user provides input to associate the image with the user&#39;s profile, whereby the image would be included in the pre-stored images in the data store  115  as part of the user&#39;s profile. 
     In any event, if the image captured in the block  205  does not match any of the pre-stored images, the process  200  continues in a block  215 . In the block  215 , the computing device  105  activates anti-theft measures as described in  FIG. 3  and the process  200  ends. 
     If the image captured in the block  205  is determined in the block  210  to match any of the stored images, the process  200  continues in a block  220 . The data store  115  typically stores a plurality of user profiles. Each profile may store various preferences and/or usage data for a particular user, e.g., settings for seat positions, mirror positions, steering wheel positions, saved destinations, records of frequently traveled-to destinations, frequently traveled routes, frequent times of travel, etc. The settings may also include a maximum operating speed for the vehicle  101  for, e.g., an adolescent user. In the block  220 , the computing device  105  searches the data store  115  for the seat position setting for the current user and moves a vehicle seat to the seat position setting. 
     Next, in a block  225 , the computing device  105  searches the data store  115  for the mirror position setting for the current user and moves vehicle mirrors to the mirror position setting. 
     Next, in a block  230 , the computing device  105  searches the data store  115  for the steering wheel position setting for the current user and moves a steering wheel to the steering wheel position setting. 
     Next in a block  232 , the computing device  105  searches the data store  115  for a personalized voice message for the current user and plays the personalized voice message over a set of vehicle speakers. For example, such a voice mail message could welcome a user to the vehicle, provide the user with personalized information, such as traffic information relating to a usual destination, weather information, reminders concerning vehicle status (e.g., fuel level, tire pressure, and the like), etc. 
     Next, in a block  235 , the computing device  105  searches the data store  115  for a predicted destination. The computing device  105  generates the predicted destination based on one or more criteria, e.g., destinations associated with the user&#39;s profile, and/or factors indicating that the user may be traveling to a particular destination, e.g., time of day, day of the week, attire (e.g., a person wearing a business suit may be headed to an office destination, but a person wearing blue jeans may be added to a recreational destination), etc. For example, if the time of day is morning and the day of the week is a weekday, the computing device  105  may predict a destination corresponding to the user&#39;s place of work. Similarly, if the time of day is evening, the computing device  105  may predict the saved destination corresponding to the user&#39;s place of residence. 
     Next, in a block  240 , after predicting the destination based on the image captured in the block  205 , the computing device  105  receives traffic data from the network  120 , e.g. from the remote server  125  and/or a remote site  160 . The traffic data may come from any suitable source, e.g., a manufacturer&#39;s crowdsourced proprietary database or a third-party database. The traffic data may include data relating to roadwork or construction on nearby roads and/or reported accidents. 
     Next, in a block  245 , the computing device  105  generates a traffic report based on the traffic data and the predicted destination. Specifically, the computing device  105  determines a route from the user&#39;s current location to the destination, and based on the traffic data, determines the route that minimizes any or all of, e.g., distance traveled, time to destination, avoidance of highways, etc. The route may be determined to avoid areas congested with traffic, allowing the vehicle  101  to maintain a substantially constant speed on the route. For example, the computing device  105  may use data on local speed limits and stop light timing to determine a route that reduces the number of times that the vehicle  101  must stop or slow down, leading to increased fuel efficiency. 
     Next, in a block  250 , the computing device  105  then presents the traffic data as a traffic report on a vehicle display. The vehicle display may be of any suitable type, e.g., a touchscreen display in a vehicle console. The traffic data may be received from, e.g. the server  125  may be updated with real-time or substantially real-time traffic data that the computing device  105  may obtain via the network  120 . The computing device  105  may present the traffic report on the vehicle display as text and images and/or may read the traffic report vocally by any suitable method, e.g., using a known in-vehicle human machine interface (HMI) such as text-to-speech software and an audio speaker, display on an in-vehicle display monitor, etc. 
     Next, in a block  255 , the computing device  105  receives weather data from the network  120  and present the weather data as a weather report on the vehicle display. The computing device  105  may also or alternatively read the weather report vocally. The weather data may be received from any suitable source, e.g., the remote site  160 . 
     Next, in a block  260 , the computing device  105  receives data from other personalized applications, e.g., news, sports, the user&#39;s schedule, and presents a report on the vehicle display. The computing device  105  may also or alternatively read the report vocally. 
     Next, in a block  270 , the computing device  105  uploads the user&#39;s image to the remote site  160 , e.g. the social media site or program, and the process  200  ends. The user may include a comment or status update with the image. 
       FIG. 3  shows a process  300  for an anti-theft measure based on user facial recognition. The process  300  starts in a block  305 , where the data collector  110  captures the image of the user and stores the image in the data store  115 . 
     Next, in a block  310 , the computing device  105  searches the data store  115  for electronic mail addresses for all user profiles. 
     Next, in a block  315 , the computing device  105  searches the data store  115  for phone numbers for all user profiles. 
     Next, in a block  320 , the computing device  105  sends an electronic mail message including the image over the network  120  to all of the electronic mail addresses found at the block  310 . 
     Next, in a block  325 , the computing device  105  sends a text message including the image over the network  120  to all of the phone numbers found at the block  315 , and the process  300  ends. 
     As used herein, the adverb “substantially” modifying an adjective means that a shape, structure, measurement, value, calculation, etc. may deviate from an exact described geometry, distance, measurement, value, calculation, etc., because of imperfections in materials, machining, manufacturing, sensor measurements, computations, processing time, communications time, etc. 
     Computing devices  105  generally each include instructions executable by one or more computing devices such as those identified above, and for carrying out blocks or steps of processes described above. Computer-executable instructions may be compiled or interpreted from computer programs created using a variety of programming languages and/or technologies, including, without limitation, and either alone or in combination, Java™, C, C++, Visual Basic, Java Script, Perl, HTML, etc. In general, a processor (e.g., a microprocessor) receives instructions, e.g., from a memory, a computer-readable medium, etc., and executes these instructions, thereby performing one or more processes, including one or more of the processes described herein. Such instructions and other data may be stored and transmitted using a variety of computer-readable media. A file in the computing device  105  is generally a collection of data stored on a computer readable medium, such as a storage medium, a random access memory, etc. 
     A computer-readable medium includes any medium that participates in providing data (e.g., instructions), which may be read by a computer. Such a medium may take many forms, including, but not limited to, non-volatile media, volatile media, etc. Non-volatile media include, for example, optical or magnetic disks and other persistent memory. Volatile media include dynamic random access memory (DRAM), which typically constitutes a main memory. Common forms of computer-readable media include, for example, a floppy disk, a flexible disk, hard disk, magnetic tape, any other magnetic medium, a CD-ROM, DVD, any other optical medium, punch cards, paper tape, any other physical medium with patterns of holes, a RAM, a PROM, an EPROM, a FLASH-EEPROM, any other memory chip or cartridge, or any other medium from which a computer can read. 
     With regard to the media, processes, systems, methods, etc. described herein, it should be understood that, although the steps of such processes, etc. have been described as occurring according to a certain ordered sequence, such processes could be practiced with the described steps performed in an order other than the order described herein. It further should be understood that certain steps could be performed simultaneously, that other steps could be added, or that certain steps described herein could be omitted. For example, in the process  200 , one or more of the steps  220 - 270  could be omitted, of the steps could be executed in a different order than shown in  FIG. 2 . In other words, the descriptions of systems and/or processes herein are provided for the purpose of illustrating certain embodiments, and should in no way be construed so as to limit the disclosed subject matter. 
     Accordingly, it is to be understood that the present disclosure, including the above description and the accompanying figures and below claims, is intended to be illustrative and not restrictive. Many embodiments and applications other than the examples provided would be apparent to those of skill in the art upon reading the above description. The scope of the invention should be determined, not with reference to the above description, but should instead be determined with reference to claims appended hereto and/or included in a non-provisional patent application based hereon, along with the full scope of equivalents to which such claims are entitled. It is anticipated and intended that future developments will occur in the arts discussed herein, and that the disclosed systems and methods will be incorporated into such future embodiments. In sum, it should be understood that the disclosed subject matter is capable of modification and variation.