Patent Publication Number: US-8989952-B2

Title: System and method for detecting vehicle crash

Description:
The present application claims priority from: U.S. Provisional Application No. 61/740,814 filed Dec. 21, 2012; U.S. Provisional Application No. 61/740,831 filed Dec. 21, 2012; U.S. Provisional Application No. 61,740,851 filed Dec. 21, 2012; and U.S. Provisional Application No. 61/745,677 filed Dec. 24, 2012, the entire disclosures of which are incorporated herein by reference. The present application is a continuation-in-part of U.S. application Ser. No. 14/072,231 filed Nov. 5, 2013, and is a continuation-in-part of U.S. application Ser. No. 14/095,156 filed Dec. 3, 2013, the entire disclosures of which are incorporated herein by reference. 
    
    
     BACKGROUND 
     Vehicle telematics is the technology of sending, receiving and storing information to and from vehicles and is generally present (at least to a limited extent) in the automotive marketplace today. For example, both General Motors (through their OnStar offering) and Mercedes Benz (through their Tele-Aid and more recent mbrace system offering) have long offered connected-vehicle functionality to their customers. Both of these offerings make use of the data available on a vehicle&#39;s CAN bus, which is specified in the OBD-II vehicle diagnostics standard. For example, the deployment of an airbag, which suggests that the vehicle has been involved in a crash, may be detected by monitoring the CAN bus. In this event, a digital wireless telephony module that is embedded in the vehicle and connected to the vehicle&#39;s audio system (i.e., having voice connectivity) can initiate a phone call to a telematics service provider (TSP) to “report” the crash. Vehicle location may also be provided to the TSP using the vehicle&#39;s GPS functionality. Once the call is established, the TSP representative may attempt to communicate with the vehicle driver, using the vehicle&#39;s audio system, to assess the severity of the situation. Assistance may thus be dispatched by the TSP representative to the vehicle as appropriate. 
     Historically, these services were focused entirely on driver and passenger safety. These types of services have expanded since their initial roll-out, however, and now offer additional features to the driver, such as concierge services. The services, however, remain mainly focused on voice based driver to call center communication, with data services being only slowly introduced, hindered by low bandwidth communication modules, high cost and only partial availability on some model lines. 
     As a result, while generally functional, vehicle telematics services have experienced only limited commercial acceptance in the marketplace. There are several reasons for this. In addition to low speeds and bandwidth, most vehicle drivers (perhaps excluding the premium automotive market niche) are reluctant to pay extra for vehicle telematics services, either in the form of an upfront payment (i.e., more expensive vehicle) or a recurring (monthly/yearly) service fee. Moreover, from the vehicle manufacturer&#39;s perspective, the services require additional hardware to be embedded into the vehicle, resulting in extra costs on the order of $250 to $350 or more per vehicle which cannot be recouped. Thus, manufacturers have been slow to fully commit to or invest in the provision of vehicle telematics equipment in all vehicles. 
     There have been rudimentary attempts in the past to determine when a smartphone is in a moving vehicle. Wireless service provider AT&amp;T, Sprint and Verizon, for example, offer a smartphone application that reacts in a specific manner to incoming text messages and voice calls when a phone is in what AT&amp;T calls DriveMode™. With the AT&amp;T DriveMode application, a wireless telephone is considered to be in “drive mode” when one of two conditions are met. First, the smartphone operator can manually turn on the application, i.e., she “tells” the application to enter drive mode. Alternatively, when the DriveMode application is in automatic on/off mode and the smartphone GPS sensor senses that the smartphone is travelling at greater than 25 miles per hour, the GPS sensor so informs the DriveMode application, the DriveMode application concludes that the smartphone is in a moving vehicle, and drive mode is entered. 
     Both of these paths to engaging the AT&amp;T DriveMode application—the “manual” approach to entering drive mode and the “automatic” approach to entering drive mode—are problematic. First, if the smartphone operator forgets or simply chooses not to launch the DriveMode application prior to driving the vehicle when the application is in manual mode then the application will not launch. Second, in automatic on/off mode AT&amp;T&#39;s use of only the GPS sensor to determine when a smartphone is in a moving vehicle is problematic for a number of reasons. First, the speed threshold of the application is arbitrary, meaning that drive mode will not be detected/engaged at less than 25 mph. If the vehicle is stopped in traffic or at a traffic signal, for example, then the DriveMode application may inadvertently terminate. Second, and perhaps more importantly, AT&amp;T&#39;s DriveMode application requires that the smartphone&#39;s GPS functionality be turned on at all times. Because the use of a smartphone&#39;s GPS sensor is extremely demanding to the battery resources of a smartphone, this requirement severely undermines the usefulness of AT&amp;T&#39;s application. Thirdly this method does not differentiate between the type of vehicle that the phone is in, e.g. a bus, a taxi or a train and therefore allows no correlation between the owner of the phone and her driving situation. For the classic embedded telematics devices to be replaces by smartphones it is important to correlate the driver and smartphone owner with her personal vehicle. Only then the smartphone can truly take the functional role of an embedded telematics device in a vehicle. 
     The main justification premise for a connected embedded device is the ability to not only detect an accident, but to autonomously call for help to either a privately operated emergency response center or 911. In fact, this safety function has been the main driver behind the last fifteen years of installing embedded communication devices in vehicles through major vehicle manufacturers. What is needed is a delivery of such a safety functionality without the need for any embedded device, thus allowing millions of drivers the safety benefit of automatic crash notification without the need for an expensive embedded device and a costly subscription. What is needed is an improved method and apparatus of determining, via a communication device, whether a vehicle has crashed. 
     SUMMARY 
     The present invention provides an improved method and apparatus of determining, via a communication device, whether a vehicle has crashed. 
     Various embodiments described herein are drawn to a device for use with a vehicle. The device includes a mode-determining component, a first detecting component and a second detecting component. The mode-determining component can generate an in-vehicle signal. The first detecting component can detect a first parameter and can generate a first detector signal based on the first detected parameter. The second detecting component can detect a second parameter and can generate a second detector signal based on the second detected parameter. The mode-determining component can further generate a crash mode signal based on the in-vehicle signal, the first detector signal and the second detector signal. 
    
    
     
       BRIEF SUMMARY OF THE DRAWINGS 
       The accompanying drawings, which are incorporated in and form a part of the specification, illustrate an exemplary embodiment of the present invention and, together with the description, serve to explain the principles of the invention. In the drawings: 
         FIGS. 1A-B  are planar views of an interior of a vehicle at a times t 0  and t 1 , respectively; 
         FIG. 2  illustrates an example device for detecting a crash in accordance with aspects of the present invention; 
         FIG. 3  illustrates an example method of detecting a vehicle crash in accordance with aspects of the present invention; 
         FIG. 4  illustrates an example parameter-detecting component in accordance with aspects of the present invention; and 
         FIG. 5  illustrates a plurality of example functions corresponding to parameters detected by an example device in accordance with aspects of the present invention. 
     
    
    
     DETAILED DESCRIPTION 
     Aspects of the present invention are drawn to a system and method for detecting a vehicle crash. 
     As used herein, the term “smartphone” includes cellular and/or satellite radiotelephone(s) with or without a display (text/graphical); Personal Communications System (PCS) terminal(s) that may combine a radiotelephone with data processing, facsimile and/or data communications capabilities; Personal Digital Assistant(s) (PDA) or other devices that can include a radio frequency transceiver and a pager, Internet/Intranet access, Web browser, organizer, calendar and/or a global positioning system (GPS) receiver; and/or conventional laptop (notebook) and/or palmtop (netbook) computer(s), tablet(s), or other appliance(s), which include a radio frequency transceiver. As used herein, the term “smartphone” also includes any other radiating user device that may have time-varying or fixed geographic coordinates and/or may be portable, transportable, installed in a vehicle (aeronautical, maritime, or land-based) and/or situated and/or configured to operate locally and/or in a distributed fashion over one or more location(s). 
     Some conventional communication devices may detect a vehicle crash, and then switch to operate in a “crash mode.” While in a crash mode, some functionalities of the communication device may be activated whereas other functionalities may be deactivated. For example, in a crash mode, a communication device may automatically contact emergency services and provide geodetic location information such that the emergency services can respond to the vehicle crash. 
     Conventional communication devices may detect a vehicle crash by way of monitoring a single parameter. In one example of a conventional communication device, a vehicle crash may be detected by monitoring declaration. If a rapid deceleration is detected, and which corresponds to a previously known deceleration or family of decelerations associated with a vehicle crashing, the communication device may determine that a vehicle has been in a crash. However, such a conventional system may detect a vehicle crash when there in fact has not been a vehicle crash, i.e., results in a false-positive. This situation may occur for example if the communication device itself is dropped by the user, and the rapid deceleration of the communication device hitting the ground emulates a rapid deceleration associated with a vehicle crash. 
     In another example of a conventional communication device, a vehicle crash may be detected by monitoring vibrations of the chassis of the vehicle associated with deployment of an airbag. If a vibration is detected, and which corresponds to a previously known vibration or family of vibrations associated with the deployment of an airbag in a vehicle, the communication device may determine that a vehicle has been in a crash. However, such a conventional system may detect a vehicle crash when there in fact is not been vehicle crash, i.e., results in a false-positive. This situation may occur for example if the communication device is near some other event, that is not a vehicle crash, but that emulates the vibrations associated with the deployment of an airbag. 
     In another example of a conventional communication device, a vehicle crash may be detected by monitoring an on-board diagnostic (OBD) system. For example, the OBD may monitor whether the airbag has been deployed, or whether there has been a rapid deceleration followed by a total stoppage (zero measured velocity). However, if the OBD is not connected directly connected to a communication device when the vehicle crashes, then information relating to the vehicle crash as detected by the OBD cannot be easily and quickly relayed outside of the vehicle, e.g. to emergency services. 
     Aspects of the present invention reduce the likelihood of obtaining a false-positive determination of a vehicle crash without connecting to an OBD. In accordance with aspects of the present invention a vehicle crash may be identified by a communication device, e.g., a smartphone, within the vehicle at the time of the vehicle crash. First, the communication device determines whether it is located in a vehicle. This first determination will greatly decrease the number of false-positive vehicle crash detections. Then the communication device will detect at least two parameters associated with a vehicle crash. If, once in the vehicle, the communication device detects values of at least two parameters that correspond to known values of known parameters associated with a vehicle crash, it may determine that the vehicle has been in a crash. The detection of at least two parameters further decreases the number of false-positive vehicle crash detections. 
     These aspects will now be described in more detail with reference to  FIGS. 1A-4 . 
       FIG. 1A  is a planar view of an interior of a vehicle  102  at a time t 0 . A position  104  represents the location of a smartphone within vehicle  102 . A superposition of magnetic fields at position  104  is represented by field lines  106 . A superposition of sound at position  104  is represented by lines  108 . Again, in accordance with aspects of the present invention, parameters such as magnetic fields at position  104  and sound at position  104  may be detected by a communication device of person in vehicle  102  in order to detect a crash of vehicle  102 . The mode of operation of the communication device may be set to vehicle mode, by any known method. 
     For purposes of discussion, consider the situation at some point in time t 1  after time t 0 , wherein vehicle  102  crashes. This will now be described with further reference to  FIG. 1B . 
       FIG. 1B  is a planar view of an interior of a vehicle  102  at a time t 1 . A position  104  represents the location of a smartphone within vehicle  102 . In this figure, an airbag  110  has deployed as a result of vehicle  102  crashing. Deployment of airbag  110  generates a specific magnetic field as represented by field lines  112 . Further, deployment of airbag  110  generates a shockwave (specific vibrations) that travels throughout the chassis of vehicle  102  as represented by the wavy lines, a sample of which is indicated as wavy lines  114 . In accordance with aspects of the present invention, a communication device may be able to detect the crash of vehicle  102  based on being in the vehicle mode and based on detecting two parameters, in this example vibrations and a magnetic field associated with deployment of airbag  110 . 
     An example system and method for detecting a vehicle crash in accordance with aspects of the present invention will now be described with additional reference to  FIGS. 2-4 . 
       FIG. 2  illustrates an example device  202  in accordance with aspects of the present invention. 
       FIG. 2  includes a device  202 , a database  204 , a field  206  and a network  208 . In this example embodiment, device  202  and database  204  are distinct elements. However, in some embodiments, device  202  and database  204  may be a unitary device as indicated by dotted line  210 . 
     Device  202  includes a field-detecting component  212 , an input component  214 , an accessing component  216 , a comparing component  218 , an identifying component  220 , a parameter-detecting component  222 , a communication component  224 , a verification component  226  and a controlling component  228 . 
     In this example, field-detecting component  212 , input component  214 , accessing component  216 , comparing component  218 , identifying component  220 , parameter-detecting component  222 , communication component  224 , verification component  226  and controlling component  228  are illustrated as individual devices. However, in some embodiments, at least two of field-detecting component  212 , input component  214 , accessing component  216 , comparing component  218 , identifying component  220 , parameter-detecting component  222 , communication component  224 , verification component  226  and controlling component  228  may be combined as a unitary device. Further, in some embodiments, at least one of field-detecting component  212 , input component  214 , accessing component  216 , comparing component  218 , identifying component  220 , parameter-detecting component  222 , communication component  224 , verification component  226  and controlling component  228  may be implemented as a computer having tangible computer-readable media for carrying or having computer-executable instructions or data structures stored thereon. Such tangible computer-readable media can be any available media that can be accessed by a general purpose or special purpose computer. Non-limiting examples of tangible computer-readable media include physical storage and/or memory media such as RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to carry or store desired program code means in the form of computer-executable instructions or data structures and which can be accessed by a general purpose or special purpose computer. For information transferred or provided over a network or another communications connection (either hardwired, wireless, or a combination of hardwired or wireless) to a computer, the computer may properly view the connection as a computer-readable medium. Thus, any such connection may be properly termed a computer-readable medium. Combinations of the above should also be included within the scope of computer-readable media. 
     Controlling component  228  is configured to communicate with: field-detecting component  212  via a communication line  230 ; input component  214  via a communication line  232 ; accessing component  216  via a communication line  234 ; comparing component  218  via a communication line  236 , identifying component  220  via a communication line  238 ; parameter-detecting component  222  via a communication line  240 ; communication component  224  via a communication line  242 ; and verification component  226  via a communication line  244 . Controlling component  228  is operable to control each of field-detecting component  212 , input component  214 , accessing component  216 , comparing component  218 , identifying component  220 , parameter-detecting component  222 , communication component  224  and verification component  226 . 
     Field-detecting component  212  is additionally configured to detect field  206 , to communicate with input component  214  via a communication line  246  and to communicate with comparing component  218  via a communication line  248 . Field-detecting component  212  may be any known device or system that is operable to detect a field, non-limiting examples of which include an electric field, a magnetic field, and electro-magnetic field and combinations thereof. In some non-limiting example embodiments, field-detecting component  212  may detect an amplitude of a field at an instant of time. In some non-limiting example embodiments, field-detecting component  212  may detect a field vector at an instant of time. In some non-limiting example embodiments, field-detecting component  212  may detect an amplitude of a field as a function over a period of time. In some non-limiting example embodiments, field-detecting component  212  may detect a field vector as a function over a period of time. In some non-limiting example embodiments, field-detecting component  212  may detect a change in the amplitude of a field as a function over a period of time. In some non-limiting example embodiments, field-detecting component  212  may detect a change in a field vector as a function over a period of time. Field-detecting component  212  is additionally able to generate a field signal based on the detected field. 
     Input component  214  is additionally configured to communicate with database  204  via a communication line  250  and to communicate with verification component  226  via a communication line  252 . Input component  214  may be any known device or system that is operable to input data into database  204 . Non-limiting examples of input component  214  include a graphic user interface having a user interactive touch screen or keypad. 
     Accessing component  216  is additionally configured to communicate with database  204  via a communication line  254  and to communicate with comparing component  218  via a communication line  256 . Accessing component  216  may be any known device or system that access data from database  204 . 
     Comparing component  218  is additionally configured to communicate with identifying component  220  via a communication line  258 . Comparing component  218  may be any known device or system that is operable to compare two inputs. 
     Parameter-detecting component  222  is additionally configured to communicate with field-detecting component  212  via a communication line  260 . Parameter-detecting component  222  may be any known device or system that is operable to detect a parameter, non-limiting examples of which include velocity, acceleration, geodetic position, sound, temperature, vibrations, pressure, contents of surrounding atmosphere and combinations thereof. In some non-limiting example embodiments, parameter-detecting component  222  may detect an amplitude of a parameter at an instant of time. In some non-limiting example embodiments, parameter-detecting component  222  may detect a parameter vector at an instant of time. In some non-limiting example embodiments, parameter-detecting component  222  may detect an amplitude of a parameter as a function over a period of time. In some non-limiting example embodiments, parameter-detecting component  222  may detect a parameter vector as a function over a period of time. In some non-limiting example embodiments, parameter-detecting component  222  may detect a change in the amplitude of a parameter as a function over a period of time. In some non-limiting example embodiments, parameter-detecting component  222  may detect a change in a parameter vector as a function over a period of time. 
     Communication component  224  is additionally configured to communicate with network  208  via a communication line  262 . Communication component  224  may be any known device or system that is operable to communicate with network  208 . Non-limiting examples of communication component include a wired and a wireless transmitter/receiver. 
     Verification component  226  may be any known device or system that is operable to provide a request for verification. Non-limiting examples of verification component  226  include a graphic user interface having a user interactive touch screen or keypad. 
     Communication lines  230 ,  232 ,  234 ,  236 ,  238 ,  240 ,  242 ,  244 ,  244 ,  246 ,  248 ,  250 ,  252 ,  254 ,  256 ,  258 ,  260  and  262  may be any known wired or wireless communication path or media by which one component may communicate with another component. 
     Database  204  may be any known device or system that is operable to receive, store, organize and provide (upon a request) data, wherein the “database” refers to the data itself and supporting data structures. Non-limiting examples of database  204  include a memory hard-drive and a semiconductor memory. 
     Network  208  may be any known linkage of two or more communication devices. Non-limiting examples of database  208  include a wide-area network, a local-area network and the Internet. 
       FIG. 3  illustrates an example method  300  of detecting a vehicle crash in accordance with aspects of the present invention. 
     Method  300  starts (S 302 ) and it is determined whether the device is in a vehicle (S 304 ). For example, returning to  FIGS. 1A-2 , device  202  may determine that it is in vehicle  102  by any known method, non-limiting examples of which include detecting parameters and comparing the detected parameters with those associated with vehicle  102 . Non-limiting examples of known parameters include magnetic fields in any of three dimensions, electric fields in any of three dimensions, electro-magnetic fields in any of three dimensions, velocity in any of three dimensions, acceleration in any of three dimensions, angular velocity in any of three dimensions, angular acceleration in any of three dimensions, geodetic position, sound, temperature, vibrations in any of three dimensions, pressure in any of three dimensions, biometrics, contents of surrounding atmosphere, a change in electric fields in any of three dimensions, a change in magnetic fields in any of three dimensions, a change in electro-magnetic fields in any of three dimensions, a change in velocity in any of three dimensions, a change in acceleration in any of three dimensions, a change in angular velocity in any of three dimensions, a change in angular acceleration in any of three dimensions, a change in geodetic position in any of three dimensions, a change in sound, a change in temperature, a change in vibrations in any of three dimensions, a change in pressure in any of three dimensions, a change in biometrics, a change in contents of surrounding atmosphere and combinations thereof. 
     In an example embodiment, device  202  determines whether it is in a vehicle and as described in copending U.S. application Ser. No. 14/095,156 filed Dec. 3, 2013. For example, device  202  may detect at least one of many parameters. Database  204  may have stored therein known parameters values that are indicative of being in a vehicle. Comparing component may compare signals based on the detected parameters with a previously stored signature corresponding to a vehicle in database  204 . Identifying component  220  may generate an in-vehicle signal indicating that device is in a vehicle based on the comparison by comparing component  218 . 
     If it is determined that device  202  is not in a vehicle (N at S 304 ), then method  300  may continue waiting for such a state (return to S 304 ). 
     On the other hand, if it is determined that device  202  is in a vehicle (Y at S 304 ), then a first parameter is detected (S 306 ). For example, returning to  FIG. 2 , let the parameter be a field, wherein field-detecting component  212  detects field  206 . For purposes of discussion, let field  206  include a magnetic field generated by the deployment of an airbag in response to the vehicle being involved with a crash, as discussed above with reference to  FIG. 1B . This is a non-limiting example, wherein the detected parameter may be any known detectable parameter, of which other non-limiting examples include magnetic fields in any of three dimensions, electric fields in any of three dimensions, electro-magnetic fields in any of three dimensions, velocity in any of three dimensions, acceleration in any of three dimensions, angular velocity in any of three dimensions, angular acceleration in any of three dimensions, geodetic position, sound, temperature, vibrations in any of three dimensions, pressure in any of three dimensions, biometrics, contents of surrounding atmosphere, a change in electric fields in any of three dimensions, a change in magnetic fields in any of three dimensions, a change in electro-magnetic fields in any of three dimensions, a change in velocity in any of three dimensions, a change in acceleration in any of three dimensions, a change in angular velocity in any of three dimensions, a change in angular acceleration in any of three dimensions, a change in geodetic position in any of three dimensions, a change in sound, a change in temperature, a change in vibrations in any of three dimensions, a change in pressure in any of three dimensions, a change in biometrics, a change in contents of surrounding atmosphere and combinations thereof. 
     Returning to  FIG. 3 , after the first parameter is detected (S 306 ), a second parameter is detected (S 308 ). For example, returning to  FIG. 2 , controlling component  228  may instruct at least one of field-detecting component  212  and parameter-detecting component  222  to detect another parameter. This is similar to method  300  (S 308 ) discussed above with reference to  FIG. 3 . 
     For example, returning to  FIG. 2 , controlling component  228  may instruct at least one of field-detecting component  212  and parameter-detecting component  222  to detect another parameter. 
     A magnetic field associated with the deployment of an airbag may be a relatively distinct parameter that may be used to determine whether a vehicle, of which the communication device is located, has been in a crash. However, there may be situations that elicit a false positive—e.g., a magnetic field that erroneously indicates that an airbag has been deployed and indicating a vehicle crash is actually a magnetic field associated with the operation of an automatic seat positioner within the vehicle, which has not been in a crash. As such, in order to reduce the probability of a false-positive indication that the vehicle has been in a crash, a second parameter associated with a vehicle crash may be used. Along this notion, it is an example aspect of the invention to detect a plurality of parameters associated with a vehicle crash to increase the probability of a correct identification of a vehicle crash. 
     In some embodiments, device  202  has a predetermined number of parameters to detect, wherein controlling component  228  may control such detections. For example, the first parameter to be detected (in S 306 ) may be a magnetic field associated with the deployment of an airbag, wherein controlling component  228  may instruct field-detecting component  212  to detect a magnetic field. Further, a second parameter to be detected may be another known detected parameter additionally associated with a vehicle crash, e.g., deceleration in three dimensions, wherein controlling component  228  may instruct parameter-detecting component  222  to detect the second parameter. Further parameter-detecting component  222  may be able to detect many parameters. This will be described with greater detail with reference to  FIG. 4 . 
       FIG. 4  illustrates an example parameter-detecting component  222 . 
     As shown in the figure, parameter-detecting component  222  includes a plurality of detecting components, a sample of which are indicated as a first detecting component  402 , a second detecting component  404 , a third detecting component  406  and an n-th detecting component  408 . Parameter-detecting component  222  additionally includes a controlling component  410 . 
     In this example, detecting component  402 , detecting component  404 , detecting component  406 , detecting component  408  and controlling component  410  are illustrated as individual devices. However, in some embodiments, at least two of detecting component  402 , detecting component  404 , detecting component  406 , detecting component  408  and controlling component  410  may be combined as a unitary device. Further, in some embodiments, at least one of detecting component  402 , detecting component  404 , detecting component  406 , detecting component  408  and controlling component  410  may be implemented as a computer having tangible computer-readable media for carrying or having computer-executable instructions or data structures stored thereon. 
     Controlling component  410  is configured to communicate with: detecting component  402  via a communication line  412 ; detecting component  404  via a communication line  414 ; detecting component  406  via a communication line  416 ; and detecting component  408  via a communication line  418 . Controlling component  410  is operable to control each of detecting component  402 , detecting component  404 , detecting component  406  and detecting component  408 . Controlling component  410  is additionally configured to communicate with controlling component  228  of  FIG. 2  via communication line  240  and to communicate with field-detecting component  212  of  FIG. 2  via communication line  260 . 
     The detecting components may each be a known detecting component that is able to detect a known parameter. For example each detecting component may be a known type of detector that is able to detect at least one of magnetic fields in any of three dimensions, electric fields in any of three dimensions, electro-magnetic fields in any of three dimensions, velocity in any of three dimensions, acceleration in any of three dimensions, angular velocity in any of three dimensions, angular acceleration in any of three dimensions, geodetic position, sound, temperature, vibrations in any of three dimensions, pressure in any of three dimensions, biometrics, contents of surrounding atmosphere, a change in electric fields in any of three dimensions, a change in magnetic fields in any of three dimensions, a change in electro-magnetic fields in any of three dimensions, a change in velocity in any of three dimensions, a change in acceleration in any of three dimensions, a change in angular velocity in any of three dimensions, a change in angular acceleration in any of three dimensions, a change in geodetic position in any of three dimensions, a change in sound, a change in temperature, a change in vibrations in any of three dimensions, a change in pressure in any of three dimensions, a change in biometrics, a change in contents of surrounding atmosphere and combinations thereof. For purposes of discussion, let: detecting component  402  be able to detect deceleration in three dimensions; detecting component  404  be able to detect sound; detecting component  406  be able to detect vibrations; and detecting component  408  be able to detect geodetic position. 
     In some non-limiting example embodiments, at least one of the detecting components of parameter-detecting component  222  may detect a respective parameter as an amplitude at an instant of time. In some non-limiting example embodiments, at least one of the detecting components of parameter-detecting component  222  may detect a respective parameter as a function over a period of time. 
     Each of the detecting components of parameter-detecting component  222  is able to generate a respective detected signal based on the detected parameter. Each of these detected signals may be provided to controlling component  410  via a respective communication line. 
     Controlling component  410  is able to be controlled by controlling component  228  via communication line  240 . 
     Consider the example situation where communication device  202  generates a signature of a vehicle crash, wherein field detecting component  212  detects a magnetic field associated with deployment of an airbag as discussed above with reference to  FIG. 1B , wherein detecting component  402  detects roll, pitch and yaw associated with movement of the communication device  202  during the vehicle crash and wherein detecting component  406  detects vibrations associated with a shockwave traveling through the chassis of the vehicle as a result of the deployment of the airbag as discussed above with reference to  FIG. 1B . This will be further described with reference to  FIG. 5 . 
       FIG. 5  includes a graph  500 , a graph  502 , a graph  504 , a graph  506 , a graph  508 , a graph  510 , a graph  512 , a graph  514 , and a graph  516 , each of which share a common x-axis  518  in units of seconds. Graph  500  has a y-axis  520  in units of degrees and includes a function  522 . Graph  502  has a y-axis  524  in units of degrees and includes a function  526 . Graph  504  has a y-axis  528  in units of degrees and has no function therein. Graph  506  has a y-axis  530  in units of m/s 2 , and includes a function  532 . Graph  508  has a y-axis  534  in units of m/s 2  and includes a function  536 . Graph  510  has a y-axis  538  in units of m/s 2  and includes a function  540 . Graph  512  has a y-axis  542  in units of μT and includes a function  544 . Graph  514  has a y-axis  546  in units of μT and includes a function  548 . Graph  516  has a y-axis  550  in units of μT and includes a function  552 . 
     Function  522  corresponds to the angular acceleration in a roll direction relative to parameter-detecting component  222 . Function  526  corresponds to the angular acceleration in a yaw direction relative to parameter-detecting component  222 . As there is no recorded function that corresponds to the angular acceleration in a pitch direction relative to parameter-detecting component  222 , in this example, no angular acceleration in a pitch direction relative to parameter-detecting component  222  was detected. Function  532  corresponds to the acceleration in an x-direction relative to parameter-detecting component  222 . Function  536  corresponds to the acceleration in a y-direction relative to parameter-detecting component  222 . Function  540  corresponds to the acceleration in a z-direction relative to parameter-detecting component  222 . Function  544  corresponds to the magnitude of B in an x-direction relative to field-detecting component  212 . Function  548  corresponds to the magnitude of B in a y-direction relative to field-detecting component  212 . Function  552  corresponds to the magnitude of B in a z-direction relative to field-detecting component  212 . 
     A sudden change in the roll manifests as curve  554  in function  522 . A sudden change in the yaw manifests as transient  556  in function  526 . A sudden change in acceleration manifests as transient  558  in function  532 , as transient  560  in function  536  and as transient  562  in function  540 . A sudden change in the magnetic field manifests as transient  564  in function  544 , as small change  566  in function  548  and as transient  568  in function  552 . These changes and transients in functions  522 ,  526 ,  532 ,  536 ,  540 ,  544 ,  548  and  552  may be indicative of an event. 
     For purposes of discussion, let these changes and transients in functions  522 ,  526 ,  532 ,  536 ,  540 ,  544 ,  548  and  552  correspond to communication device  202  changing position as a result of a vehicle crash. Specifically, let curve  554  in function  522  transient  556  in function  526  correspond to a sudden change in position of communication device  202  when the vehicle crashes. Further, let transient  558  in function  532 , transient  560  in function  536  and transient  562  in function  540  correspond to a shockwave within the chassis associated with deployment of the airbag when the vehicle crashes. Finally, let transient  564  in function  544 , change  566  in function  548  and transient  568  in function  552  correspond to a magnetic field associated with deployment of the airbag when the vehicle crashes. 
     In this example, spike  570  in function  532 , spike  572  in function  536  and spike  574  in function  540  correspond to the dropping of communication device into position to start the crash test of the vehicle. 
     In this example therefore, the vehicle crash may have a signature based on functions  522 ,  526 ,  532 ,  536 ,  540 ,  544 ,  548  and  552 , having tell-tail changes and transients  554 ,  556 ,  558 ,  560 ,  562 ,  564 ,  566  and  568 , respectively. In some embodiments, field-detecting component  212  may additionally process any of functions  522 ,  526 ,  532 ,  536 ,  540 ,  544 ,  548  and  552  and combinations thereof to generate such a signature. Non-limiting examples of further processes include averaging, adding, subtracting, and transforming any of functions  612 ,  614 ,  616 ,  618  and combinations thereof. 
     Returning to  FIG. 3 , after the first two parameters are detected (S 306  and S 308 ), a crash probability. C p , is generated (S 310 ). For example, first a previously-stored signature (or signatures) may be retrieved, which is based on parameters associated with a vehicle crash. Then a crash signature is generated based on the detected parameters. Then the crash signature is compared with the previously-stored signature (or signatures), wherein the comparison is used to generate the crash probability C p . The crash probability C p  is a value that indicates the likelihood that the vehicle has crashed based on the similarity of the previously-stored signature and the newly-generated signature. In essence, it is determined whether the previously detected parameters associated with a previous vehicle crash (or previous vehicle crashes) are similar to the newly detected parameters. 
     In an example embodiment, the previously-stored signature may be stored in database  204 . A crash signature may be created by any known system or method and may be based detected parameters associated with previously recorded crashes. For example, crash signatures may be created based on previously recorded crashes from controlled crashes in a testing environment, i.e., test-crashes, and uncontrolled crashes, e.g., automobile accidents. 
     In some example embodiments, a plurality of crash signatures are stored in database  204 , wherein each crash signature is associated with a particular make, model and year vehicle. These crash signatures may be generated from previously recorded crashes from controlled crashes and uncontrolled crashes. 
     In some example embodiments, a plurality of crash signatures are stored in database  204 , wherein each crash signature is associated with many different makes, models and years of vehicles. These crash signatures may be generated from previously recorded crashes from controlled crashes and uncontrolled crashes. 
     In some example embodiments, a plurality of crash signatures are stored in database  204 , wherein each crash signature is associated with a particular type of vehicle crash, e.g., front, rear or side. These crash signatures may be generated from previously recorded crashes from controlled crashes and uncontrolled crashes. 
     In some example embodiments, a plurality of crash signatures are stored in database  204 , wherein each crash signature is associated with a combination of: many different makes, models and years of vehicle and with a particular type of vehicle crash, e.g., front, rear or side. These crash signatures may be generated from previously recorded crashes from controlled crashes and uncontrolled crashes. 
     Non-limiting examples of detected parameters for which each crash signature is based include at least one of magnetic fields in any of three dimensions, electric fields in any of three dimensions, electro-magnetic fields in any of three dimensions, velocity in any of three dimensions, acceleration in any of three dimensions, angular velocity in any of three dimensions, angular acceleration in any of three dimensions, geodetic position, sound, temperature, vibrations in any of three dimensions, pressure in any of three dimensions, biometrics, contents of surrounding atmosphere, a change in electric fields in any of three dimensions, a change in magnetic fields in any of three dimensions, a change in electro-magnetic fields in any of three dimensions, a change in velocity in any of three dimensions, a change in acceleration in any of three dimensions, a change in angular velocity in any of three dimensions, a change in angular acceleration in any of three dimensions, a change in geodetic position in any of three dimensions, a change in sound, a change in temperature, a change in vibrations in any of three dimensions, a change in pressure in any of three dimensions, a change in biometrics, a change in contents of surrounding atmosphere and combinations thereof. 
     As for how a crash signature is generated, in some embodiments it is a signal output from a detecting component that is capable of detecting a parameter. A crash signature may be a composite detected signal that is based on any of an individual detected signal, and combination of a plurality of detected signals. In some embodiments, any of the individual detected signals and combinations thereof may be additionally processed to generate a crash. Non-limiting examples of further processes include averaging, adding, subtracting, and transforming any of the individual detected signals and combinations thereof. For purposes of discussion, consider the situation where a vehicle is crash-tested and parameters are detected to generate a crash signature. In this example, let the crash signature be based on: a detected magnetic field associated with deployment of the airbag during the crash; a detected deceleration in three dimensions during the crash; a detected sound during the crash; and detected vibrations during the crash. Further, in this example, let the crash signature be the five separate signals, such that future comparisons with other crash signatures will compare signals of similar parameters. 
     Returning to  FIG. 2 , previously stored crash signatures are stored in database  204  as a priori information. 
     Controlling component  228  may then instruct access component  216  to retrieve a previously-stored signature, from database  204  and to provide the previously-stored signature to comparing component  218 . In some embodiments, a single previously-stored signature is retrieved, wherein in other embodiments, more than one previously-stored signature may be received. 
     Controlling component  228  may then instruct comparing component  218  to generate a crash probability, C p , indicating a probability that the vehicle crashed. 
     In embodiments where a single previously-stored signature is retrieved, the newly generated signature may be compared with the single previously-stored signature. The crash probability C p  may then be generated based on the similarity between the newly generated signature and the single previously-stored signature. 
     In some embodiments where plural previously-stored signatures are retrieved, the newly generated signature may be compared each previously-stored signature in a serial manner. The crash probability C p  may then be generated based on the similarity between the newly generated signature and the single previously-stored signature of which is most similar to the newly generated signature. 
     In some embodiments where plural previously-stored signatures are retrieved, the newly generated signature may be compared each previously-stored signature in a parallel manner. The crash probability C p  may then be generated based on the similarity between the newly generated signature and the single previously-stored signature of which is most similar to the newly generated signature. 
     In an example embodiment, the newly generated signature is compared with a single previously-stored signature. If the newly generated signature is exactly the same as the previously-stored signature, then the generated crash probability will be 1, thus indicating that the vehicle has crashed. Variations between the newly generated signature and the previously-stored signature will decrease the generated crash probability, thus decreasing the likelihood that the vehicle has crashed. Any known method of comparing two signatures to generate such a probability may be used. 
     In an example embodiment, a comparison is made between similar parameter signals. For example, let a previously-stored signature be a function corresponding to a previously-detected magnetic field and a second function corresponding to a previously-detected deceleration in three dimensions, and let a newly-detected signature be a function corresponding to a newly-detected magnetic field and a second function corresponding to a newly-detected deceleration in three dimensions. The comparison would include a comparison of the function corresponding to the previously-detected magnetic field and the function corresponding to the newly-detected magnetic field and a comparison of the second function corresponding to a previously-detected deceleration in three dimensions and the second function corresponding to a newly-detected deceleration in three dimensions. 
     Controlling component  228  may then provide the crash probability C p  to identifying component  220  via communication line  258 . 
     Returning to  FIG. 3 , it is then determined whether the generated crash probability C p  is greater than or equal to a predetermined probability threshold, T p  (S 312 ). For example, identifying component  220  may have a predetermined probability threshold T p  stored therein. The probability threshold T p  may be established to take into account acceptable variations in detected parameters. For example, all vehicles may have varying unique parameter signatures, e.g., magnetic signatures, thermal signatures, acoustic signatures, etc. However, the corresponding parameter signatures of all vehicles in a crash may be considered somewhat similar. These similarities may be taken into account when setting the probability threshold T p . 
     Clearly, if the probability threshold T p  is set to 1, this would only be met if newly generated signature is exactly the same as the previously-stored signature (or one of the previously stored signatures), thus indicating that the vehicle has crashed. Further, this threshold would not be met if the sensors did not detect the exact parameters, which does not generally represent a real world scenario. On the contrary, if the probability threshold T p  is decreased, it would take into account variations in the detected parameters. Further, if the probability threshold T p  is decreased further, it may take into account variations in a class of vehicle crashes, e.g., difference vehicles, or crashes from various angles. 
     In an example embodiment, identifying component  220  determines whether the crash probability C p  generated by comparing component  218  is greater than or equal to the predetermined probability threshold T p . In this case, identifying component  220  is a probability-assessing component that generates a probability of a specific mode based on a comparison or comparison signal. 
     Returning to  FIG. 3 , if it is determined that the generated crash probability is greater than or equal to the predetermined probability threshold (Y at S 312 ), then the device is operated in a crash mode (S 314 ). For example, consider the situation where a person carrying device  202  is driving in vehicle  102 , which crashes. Identifying component  220  has determined that the newly detected signature associated with the detected parameters from the crash matches a previously-stored signature for a vehicle crash. In such a case, identifying component  220  provides a crash mode signal to controlling component  228 , via communication line  238 , indicating device  202  should operate in a crash mode. Further, for purposes of discussion, let the crash mode be such a mode wherein predetermined functions of device  202  may be activated, such as automatically contacting emergency services. 
     In this situation, identifying component  220  acts as a mode-determining component and has generated an in-vehicle signal indicating that device  202  is in a vehicle. Further field-detecting component  212  has generated a detector signal based on a first detected parameter, in this example, a detected magnetic field associated with the deployment of an airbag. Additionally, parameter-detecting component  222  has generated a detector signal based on a second detected parameter, in this example, a detected deceleration. Finally, identifying component  220  generates the crash mode signal based on the in-vehicle signal, the signal based on the first parameter and the signal based on the second parameter. Having the crash mode signal being based on the in-vehicle signal, and both detector signals greatly decreases the chances of false-positive identifications of a vehicle crash. Further, this system is able to generate an accurate crash mode signal without accessing the OBD. 
     Returning to  FIG. 3 , once the device is operated in the crash mode (S 314 ), method  300  stops (S 328 ). 
     If it is determined that the generated crash probability is less than the predetermined probability threshold (N at S 312 ), it is determine whether an additional parameter is to be detected (S 316 ). For example, returning to  FIG. 3 , as discussed previously, parameter-detecting component  222  may be able to detect a plurality of parameters. In some embodiments, all parameters are detected at once, whereas in other embodiments some parameters are detected at different times. 
     Consider the situation where an initially generated crash probability is based only on a newly-detected magnetic field as detected by field-detecting component  212  and on a newly-detected deceleration in three dimensions as detected by detecting component  302 . Further, for purposes of discussion, let the generated crash probability be less than the predetermined probability threshold. In such a case, if more parameters had been detected, they may be used to further indicate that the vehicle has crashed. 
     Returning to  FIG. 3 , if an additional parameter is to be detected (Y at S 316 ), then an additional parameter is detected (S 318 ). For example, controlling component  228  may instruct parameter-detecting component  222  to provide additional information based on additionally detected parameters to field-detecting component  212 . 
     Returning to  FIG. 3 , after the additional parameter is detected (S 318 ), the crash probability is updated (S 320 ). For example, the new signature may be generated in a manner similar to the manner discussed above in method  300  (S 310 ) of  FIG. 3 . Controlling component  228  may then instruct access component  216  to retrieve the previously-stored signature, e.g., from method  300  of  FIG. 3 , from database  204  and to provide the previously-stored signature to comparing component  218 . 
     Controlling component  228  may then instruct comparing component  218  to generate an updated crash probability, C pu , indicating a probability that the vehicle has crashed. In an example embodiment, the newly generated signature is compared with the previously-stored signature. Again, any known method of comparing two signatures to generate such a probability may be used. 
     In an example embodiment, a comparison is made between similar parameter signals. For purposes of discussion, let the previously generated crash probability C p  be based on the newly-detected magnetic field as detected by field-detecting component  212  and on a newly-detected deceleration in three dimensions as detected by detecting component  402 . Now, let the updated, generated crash probability C pu  be based on: 1) the newly-detected magnetic field as detected by field-detecting component  212 ; 2) the newly-detected deceleration in three dimensions as detected by detecting component  402 ; and 3) a newly-detected vibration as detected by detecting component  406 . 
     The new comparison may include: a comparison of the function corresponding to the previously-detected magnetic field and the function corresponding to the newly-detected magnetic field; a comparison of the second function corresponding to a previously-detected deceleration in three dimensions and the second function corresponding to the newly-detected deceleration in three dimensions; and a comparison of the second function corresponding to a previously-detected vibration and the second function corresponding to the newly-detected vibration. 
     Returning to  FIG. 3 , after the crash probability is updated (S 320 ), it is again determined whether the generated crash probability is greater than or equal to the predetermined probability threshold (S 312 ). Continuing the example discussed above, now that many more parameters have been considered in the comparison, the updated crash probability C p , which is now C pu , is greater than or equal to the probability threshold T p . For example, although the previous comparison between only two parameters provided a relatively low probability, the additional parameters greatly increased the probability. For example, consider the situation where the detected magnetic field and the detected deceleration in three dimensions are sufficiently dissimilar to the previously stored magnetic field and deceleration in three dimensions associated with a vehicle crash. However, now that more parameters are considered, e.g., sound, velocity, vibrations and change in geodetic position, it may be more likely that vehicle has, in fact, crashed. 
     Returning to  FIG. 3 , if an additional parameter is not to be detected (N at S 316 ), then the device is not operated in the crash mode (S 322 ). If the crash probability C p  is ultimately lower than the predetermined probability threshold T p , then it is determined that the vehicle has not crashed. As such, device  202  would not be operating in the crash mode. 
     Returning to  FIG. 3 , it is then determined whether the current operating mode has been switched to the crash mode (S 324 ). For example, returning to  FIG. 2 , there may be situations where a user would like device  202  to operate in a crash mode, even though device  202  is not currently operating in such a mode. In those situations, user  202  may be able to manually change the operating mode of device  202 . For example, a GUI of input component  214  may enable the user to instruct controlling component  228 , via communication line  232 , to operate in a specific mode. 
     Returning to  FIG. 3 , if it is determined that the current operating mode has been switched to the crash mode (Y at S 324 ), then the device is operated in a crash mode (S 314 ). 
     Alternatively, if it is determined that the mode has not been switched (N at S 324 ), then it is determined whether the device has been turned off (S 326 ). For example, returning to  FIG. 2 , there may be situations where a user turns off device  202  or device  202  runs out of power. If it is determined that the device has not been turned off (N at S 326 ), the process repeats and it is determined whether the device is in a vehicle (S 304 ). Alternatively, if it is determined that the device has been turned off (Y at S 326 ), the method  300  stops (S 328 ). 
     In some embodiments, when it is determined that device  202  is in a vehicle (Y at S 304 ), field-detecting component  212  and parameter-detecting component  222  may be operated to detect respective parameters at the fasted rate possible. In this manner, a crash may be accurately detected as soon as possible, but much power may be expended in device  202 . 
     In some embodiments, when it is determined that device  202  is in a vehicle (Y at S 304 ), field-detecting component  212  and parameter-detecting component  222  may be adjusted to operate to detect respective parameters at the lower rate. In this manner, a crash may be accurately detected as with some delay, but power of device  202  may be saved. In an example embodiment, a user is able to adjust the detection rate of field-detecting component  212  and parameter-detecting component  222  by way of the GUI in input component  214 . 
     Aspects of the present invention enable a communication device to accurately determines whether a vehicle as crashed without accessing the OBD of the vehicle. In particular, a communication device in accordance with aspects of the present invention can accurately detect a vehicle crash by detecting that it is in a vehicle, detecting a first parameter associated with a crash, detecting a second parameter associated with a crash, generating a crash probability and comparing the crash probability with a predetermined threshold. By detecting a crash based on being in a vehicle and based on two additionally detected parameters, the likelihood of erroneously detecting a crash is greatly reduced. 
     In the drawings and specification, there have been disclosed embodiments of the invention and, although specific terms are employed, they are used in a generic and descriptive sense only and not for purposes of limitation, the scope of the invention being set forth in the following claims.