Patent Publication Number: US-2013232101-A1

Title: Automated dynamic vehicle blind spot determination

Description:
RELATED APPLICATIONS 
     This application is a continuation of and claims priority to and claims the benefit of U.S. patent application Ser. No. 12/196,042 titled “AUTOMATED DYNAMIC VEHICLE BLIND SPOT DETERMINATION,” which was filed in the United States Patent and Trademark Office on Aug. 21, 2008, and which is incorporated herein by reference in its entirety. 
    
    
     BACKGROUND 
     The present invention relates to determining blind spot regions associated with a vehicle. More particularly, the present invention relates to automated dynamic vehicle blind spot determination. 
     Blind spots are areas around a vehicle that are not visible to a driver while the driver is seated within the driver&#39;s seat of the vehicle. Typical blind spot areas are located to the sides and back areas of the vehicle. Vehicles have rear view and side view mirrors to allow drivers to see portions of areas to the rear area and sides of vehicles. 
     BRIEF SUMMARY 
     The subject matter described herein provides dynamic vehicle blind spot determination based upon changing characteristics associated with a driver and a vehicle&#39;s surrounding environment. As the driver&#39;s orientation changes, the blind spot positions and dimensions change. For example, characteristics of the vehicle, such as seat position and seat height, and characteristics of the driver&#39;s orientation, such as height, position within a driver&#39;s seat, physical movement within the driver&#39;s seat, eye activity, head position, and other characteristics, all affect blind spot positions and dimensions. The driver&#39;s orientation is monitored and changes in the driver&#39;s orientation are automatically detected. A resulting change to the blind spot is calculated. Dangerous situation history is statistically modeled and used to predict dangerous situations based upon at least one of the automatically detected change in the driver&#39;s orientation and the calculated change to the blind spot. At least one of the calculated change to the blind spot and the predicted dangerous situation is communicated to the driver&#39;s vehicle or to another vehicle. The statistical model is updated to reflect new dangerous situations that are identified. 
     A method includes monitoring a driver&#39;s orientation within a first vehicle, detecting a change in the driver&#39;s orientation, and calculating a change to a blind spot of the first vehicle based upon the detected change in the driver&#39;s orientation. 
     A system includes a memory adapted to store vehicle characteristics associated with a first vehicle; and a processor programmed to monitor a driver&#39;s orientation within the first vehicle, detect a change in the driver&#39;s orientation, and calculate a change to a blind spot of the first vehicle based upon the detected change in the driver&#39;s orientation and the stored vehicle characteristics. 
     An alternative system includes a memory adapted to store vehicle characteristics associated with a first vehicle and a statistical model of dangerous situations; and a processor programmed to monitor a driver&#39;s orientation within the first vehicle, detect a change in the driver&#39;s orientation, detect a change in at least one of an eye position of the driver, a head position of the driver, a body position of the driver, and an activity of the driver, determine at least one characteristic associated with the first vehicle, the at least one characteristic comprising at least one of a size of the first vehicle, a mirror adjustment associated with the first vehicle, a speed of the first vehicle, a driver&#39;s seat height, a driver&#39;s seat position, and a steering angle of the first vehicle, calculate a change to at least one of a blind spot shape, blind spot dimensions, and a blind spot location of the first vehicle based upon the detected change in the driver&#39;s orientation and the determined at least one characteristic associated with the first vehicle, read the statistical model of dangerous situations from the memory, apply at least one of the detected change in the driver&#39;s orientation and the calculated change to the blind spot to the statistical model of dangerous situations, predict a dangerous situation associated with the first vehicle based upon a result of the at least one of the detected change in the driver&#39;s orientation and the calculated change to the blind spot applied to the statistical model of dangerous situations, communicate information associated with at least one of the calculated change to the blind spot and the predicted dangerous situation to at least one of the first vehicle and a second vehicle, update the statistical model of dangerous situations based upon data associated with the predicted dangerous situation, and store the updated statistical model of dangerous situations to the memory. 
     A computer program product includes a computer useable medium including a computer readable program. The computer readable program when executed on a computer causes the computer to monitor a driver&#39;s orientation within the first vehicle, detect a change in the driver&#39;s orientation, and calculate a change to a blind spot of the first vehicle based upon the detected change in the driver&#39;s orientation and the stored vehicle characteristics. 
     Those skilled in the art will appreciate the scope of the present invention and realize additional aspects thereof after reading the following detailed description of the preferred embodiments in association with the accompanying drawing figures. 
    
    
     
       BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS 
       The accompanying drawing figures incorporated in and forming a part of this specification illustrate several aspects of the invention, and together with the description serve to explain the principles of the invention. 
         FIG. 1  is a diagram of an example of a roadway environment that illustrates example initial vehicle blind spots based upon an initial driver orientation according to an embodiment of the present subject matter; 
         FIG. 2  is an example of the roadway environment of  FIG. 1  that illustrates dynamic changes to blind spot regions associated with a vehicle based upon changes in the driver&#39;s orientation within the vehicle according to an embodiment of the present subject matter; 
         FIG. 3  is an example of the roadway environment of  FIG. 1  that illustrates dynamic changes to blind spot regions associated with a vehicle based upon a driver of the vehicle turning his or her head to the right to either look into a right side mirror of the vehicle or to talk with a passenger located within a passenger seat of the vehicle according to an embodiment of the present subject matter; 
         FIG. 4  is a block diagram of an example of an implementation of a system that provides automated dynamic vehicle blind spot determination based upon changes in a driver&#39;s orientation within a vehicle according to an embodiment of the present subject matter; 
         FIG. 5  is a block diagram of an example of an implementation of a dynamic blind spot detection and prediction device that provides the automated dynamic vehicle blind spot determination and dangerous situation prediction within a system, such as the system of  FIG. 4 , according to an embodiment of the present subject matter; 
         FIG. 6  is a block diagram showing more detail associated with an example driver monitoring module, car monitoring module, and dynamic blind spot determination module of the example implementation of the dynamic blind spot detection and prediction device of  FIG. 5  according to an embodiment of the present subject matter; 
         FIG. 7  is a flow chart of an example of an implementation of a process that automatically calculates changes to a blind spot of a vehicle based upon detected changes in a driver&#39;s orientation within the vehicle according to an embodiment of the present subject matter; and 
         FIG. 8  is a flow chart of an example of an implementation of a process that automatically calculates changes to a blind spot of a vehicle by executing a probabilistic model, identifies dangerous and potentially dangerous situations, and alerts other vehicles of any identified dangerous or potentially dangerous situations according to an embodiment of the present subject matter. 
     
    
    
     DETAILED DESCRIPTION 
     The examples set forth below represent the necessary information to enable those skilled in the art to practice the invention and illustrate the best mode of practicing the invention. Upon reading the following description in light of the accompanying drawing figures, those skilled in the art will understand the concepts of the invention and will recognize applications of these concepts not particularly addressed herein. It should be understood that these concepts and applications fall within the scope of the disclosure and the accompanying claims. 
     The subject matter described herein provides dynamic vehicle blind spot determination based upon changing characteristics associated with a driver and a vehicle&#39;s surrounding environment. As the driver&#39;s orientation changes, the blind spot positions and dimensions change. For example, characteristics of the vehicle, such as seat position and seat height, and characteristics of the driver&#39;s orientation, such as height, position within a driver&#39;s seat, physical movement within the driver&#39;s seat, eye activity, head position, and other characteristics, all affect blind spot positions and dimensions. The driver&#39;s orientation is monitored and changes in the driver&#39;s orientation are automatically detected. A resulting change to the blind spot is calculated. Dangerous situation history is statistically modeled and used to predict dangerous situations based upon at least one of the automatically detected change in the driver&#39;s orientation and the calculated change to the blind spot. At least one of the calculated change to the blind spot and the predicted dangerous situation is communicated to the driver&#39;s vehicle or to another vehicle. The statistical model is updated to reflect new dangerous situations that are identified. 
     The dynamic vehicle blind spot determination described herein may be performed in real time to allow prompt notification and alerting. For purposes of the present description real time shall include any time frame of sufficiently short duration as to provide reasonable response time for information processing acceptable to a user of the subject matter described. Additionally, the term “real time” shall include what is commonly termed “near real time”—generally meaning any time frame of sufficiently short duration as to provide reasonable response time for on demand information processing acceptable to a user of the subject matter described (e.g., within a few seconds or less than ten seconds or so in certain systems). These terms, while difficult to precisely define are well understood by those skilled in the art. 
     The examples of  FIG. 1  through  FIG. 3  below provide context for the technical description that follows.  FIG. 1  through  FIG. 3  illustrate examples of dynamic changes that may occur to blind spots that are associated with a vehicle based upon changes in orientation of a driver within that vehicle. Within  FIG. 1  through  FIG. 3 , it is assumed that the vehicles described below are traveling from the right to the left within the respective illustrations. 
       FIG. 1  is a diagram of an example of a roadway environment  100  that illustrates example initial vehicle blind spots based upon an initial driver orientation. A vehicle  102 , a vehicle  104 , and a vehicle  106  are shown in association with the roadway environment  100 . The vehicle  102  is shown with visible areas and blind spot areas associated with it. 
     A visible area  108  represents a forward view range for a driver of the vehicle  102 . The visible area  108  represents approximately 180 degrees of visible area including peripheral vision capabilities of the driver and is approximately perpendicular to a direction of travel of the vehicle  102 . As can been seen from  FIG. 1 , both the vehicle  104  and the vehicle  106  are outside of the visible area  108 . In order to assist the driver of the vehicle  102  with viewing vehicles outside of the visible area  108 , the vehicle  102  is equipped with certain mirrors, such as a rear view mirror, a left side mirror, and a right side mirror. The rear view mirror, the left side mirror, and the right side mirror are not shown for ease of illustration purposes. A person of skill in the art will be able to determine appropriate locations for the rear view mirror, the left side mirror, and the right side mirror based upon the present description. 
     When the driver of the vehicle  102  uses the rear view mirror, a rear viewable area  110  may be seen by the driver of the vehicle  102 . As can be seen from  FIG. 1 , the driver of the vehicle  102  is unable to see the vehicle  104  and the vehicle  106  within the rear viewable area  110 . 
     In order to allow the driver of the vehicle  102  to see additional areas behind the visible area  108 , the left side mirror and right side mirror are used. A left rear viewable area  112  and a right rear viewable area  114  represent viewable areas behind the visible area  108  that may be seen by the driver of the vehicle  102  using the left side mirror and the right side mirror, respectively. As can be seen from  FIG. 1 , the driver of the vehicle  102  may be able to see a portion of the vehicle  106  within the left rear viewable area  112  provided by the left side mirror. However, the driver cannot see the vehicle  104  within the left rear viewable area  112 . 
     Gaps in viewable coverage of the rear view mirror, the left side mirror, and the right side mirror are considered blind spots for purposes of the present subject matter. Accordingly, a left blind spot region  116  represents a viewable gap in viewable area located outside of the visible area  108  and the left rear viewable area  112 . As a result, the driver of the vehicle  102  must turn his or her head in the direction of the vehicle  104  to be able to see the vehicle  104 . A similar right blind spot region  118  is located on the right side of the vehicle  102  outside of the visible area  108  and the right rear viewable area  114 . 
     As will be described in more detail below, the dimensions of the left blind spot region  116  and the right blind spot region  118  dynamically change in response to a variety of factors, such as the driver&#39;s orientation within the vehicle  102  and changes in the driver&#39;s orientation and activity. It should also be noted that a rear blind spot region  120  is located behind the vehicle  102 . For a given orientation of the driver within the vehicle  102 , the location and dimensions of the rear blind spot region  120  may be considered relatively fixed based upon characteristics of the vehicle  102 , such as a height and width of the vehicle  102 . As will also be described in more detail below, as the driver of the vehicle  102  changes his or her orientation within the vehicle  102 , the dimensions of the rear blind spot region  120  will also change. 
       FIG. 2  is an example of the roadway environment  100  of  FIG. 1  that illustrates dynamic changes to blind spot regions associated with the vehicle  102  based upon changes in the driver&#39;s orientation within the vehicle  102 . For purposes of the present example, it is assumed that the driver has leaned to his or her left, for example to place an elbow on an armrest on the left side door of vehicle  102 , as represented by arrow (A). It is further assumed that the driver has slouched within a driver&#39;s seat of the vehicle  102 , perhaps due to fatigue or a favorite song coming on the radio of the vehicle  102 , and that the driver is looking forward. 
     As can be seen from  FIG. 2 , as a result of the drivers change in orientation to be positioned lower and toward the left side of the vehicle  102 , yet still looking forward, the visible area  108  remains approximately perpendicular to the direction of travel of the vehicle  102 . However, incident angles of the driver&#39;s eyesight upon each of the front mirror, the left side mirror, and the right side mirror have changed. Accordingly, each of the rear viewable area  110 , the left rear viewable area  112 , and the right rear viewable area  114  have also dynamically changed in response to the change in the driver&#39;s orientation within the vehicle  102 . 
     As can be seen from  FIG. 2 , the rear viewable area  110  is shifted slightly to the right rear of the vehicle  102  as represented generally by arrow (B). Additionally, the left rear viewable area  112  has been shifted toward the right rear of the vehicle  102  and slightly narrowed. The right rear viewable area  114  has also shifted toward the right rear of the vehicle  102  and slightly expanded. However, it should be noted that because of the driver&#39;s change in physical orientation within the vehicle  102  the right rear viewable area  114  has changed such that driver can no longer see the vehicle  106  within the right rear viewable area  114 . 
     Based upon the dynamic changes in the rear viewable area  110 , the left rear viewable area  112 , and the right rear viewable area  114 , the left blind spot region  116  has expanded in size and the right blind spot region  118  has decreased in size. It should also be noted that the rear blind spot region  120  has additionally changed in dimension due to the driver slouching within the driver&#39;s seat of the vehicle  102 . As can be seen from  FIG. 2 , the rear blind spot region  120  has been lengthened relative to the length of the vehicle  102 . 
     These changes in the viewable areas and blind spots around the vehicle  102  may result in the driver&#39;s inability to see obstacles and other vehicles within the roadway environment  100  as easily as if the driver was sitting up and centered within the driver&#39;s seat of the vehicle  102 . Accordingly, for purposes of the present subject matter, such a change in the driver&#39;s orientation within the vehicle  102  may be considered a dangerous or potentially dangerous situation. 
       FIG. 3  is an example of the roadway environment  100  of  FIG. 1  that illustrates dynamic changes to blind spot regions associated with the vehicle  102  based upon the driver of the vehicle  102  turning his or her head to the right to either look into the right side mirror of the vehicle  102  or to talk with a passenger located within a passenger seat of the vehicle  102 . As can be seen from  FIG. 3 , several changes to the viewable areas and blind spot regions are illustrated. The visible area  108  is no longer approximately perpendicular to the direction of travel to the vehicle  102  within the roadway environment  100 . Accordingly, the driver of the vehicle  102  can no longer see certain areas near the left front and left side of the vehicle  102 . However, the driver can see addition areas to the right side and right rear of the vehicle  102  within the visible area  108 . 
     It should be noted that because the driver has turned his or her head to the right within the vehicle  102 , the driver can no longer see the left side mirror, even with consideration of peripheral vision capabilities of the driver. Accordingly, the left rear viewable area  112  is not depicted within  FIG. 3  at all to illustrate that the driver cannot see any areas to the left or rear of the vehicle  102  within the left side mirror. The rear viewable area  110  and the right rear viewable area  114  are also dynamically changed in dimension with associated changes to the left blind spot region  116  and the right blind spot region  118 . The dimensions of the rear blind spot region  120  will depend upon the physical orientation of the driver within the driver&#39;s seat of the vehicle  102 . 
     It should be noted that the left blind spot region  116  has been increased such that the driver of the vehicle  102  cannot see any portion of the vehicle  104  or the vehicle  106 . These changes in the viewable areas and blind spots around the vehicle  102  may result in increased risk of a collision when compared with either of the representations within  FIG. 1  or  FIG. 2 . Accordingly, for purposes of the present description this may be considered a dangerous situation. 
       FIG. 4  is a block diagram of an example of an implementation of a system  400  that provides automated dynamic vehicle blind spot determination based upon changes in a driver&#39;s orientation within a vehicle. Within the system  400 , a dynamic vehicle blind spot detection and prediction device  402  provides the dynamic vehicle blind spot detection capabilities of the present subject matter. Additionally, the dynamic blind spot detection and prediction device  402  accesses a database  404  including a statistical model  406  and a dangerous situation history repository  408  to facilitate prediction of dangerous situations within a roadway environment, such as the roadway environment  100  of  FIG. 1  though  FIG. 3  above. 
     The statistical model  406  may include any of a variety of probabilistic models, such as a Gaussian, uniform, or other stochastic/statistical model, that describes mathematical relationships between dangerous situations associated with a vehicle, such as the vehicle  102 . For example, dangerous driver characteristics for the driver of the vehicle  102 , dangerous blind spot coordinates associated with a vehicle  102  based upon the dangerous driver characteristics, dangerous information about the vehicle  102  (e.g., speed, orientation, steering angle relative to speed, etc.), and dangerous characteristics of an environment surrounding the vehicle  102 , such as dangerous weather conditions (e.g., fog, rain, etc.), and dangerous conditions associated with other surrounding vehicles, such as the vehicle  104  and the vehicle  106 , are all possible dangerous characteristics that may be modeled by the statistical model  406 . Many other dangerous characteristics are possible and all are considered within the scope of the present subject matter. 
     The dangerous situation history repository  408  stores historical information about the dangerous driver characteristics of the driver of the vehicle  102 , the dangerous blind spot coordinates associated with a vehicle  102 , the dangerous information about the vehicle  102 , and dangerous characteristics of an environment surrounding the vehicle  102 . This historical information is accessed by the dynamic vehicle blind spot detection and prediction device  402  to process the statistical model  406  to predict potential or actual dangerous situations associated with the vehicle  102 . 
     The dynamic vehicle blind spot detection and prediction device  402  communicates via a wireless network  410  with another vehicle_ 1   412  through another vehicle_N  414 . This communication includes alerting the other vehicle_ 1   412  through the other vehicle_N  414  to potential or actual dangerous situations identified by the dynamic vehicle blind spot detection and prediction device  402 . For purposes of the present description, the wireless network  410  may include any communication connection capable of providing communications between two moving vehicles. For example, the wireless network  410  may include a cellular network, direct Bluetooth connectivity, and any other wireless network or direct wireless connectivity capable of providing communication between vehicles traveling in proximity to one another. 
     As will be described in more detail below in association with  FIG. 5  through  FIG. 8 , the dynamic vehicle blind spot detection and prediction device  402  provides dynamic vehicle blind spot detection by monitoring a driver&#39;s orientation within a vehicle, such as the vehicle  102  of  FIG. 1 . The dynamic vehicle blind spot detection and prediction device  402  detects changes in a driver&#39;s orientation within the vehicle  102  and calculates a change to a blind spot of the vehicle  102  based upon the detected change in the driver&#39;s orientation within the vehicle  102 . 
     As described above, the dynamic blind spot detection and dangerous situation identification capabilities of the dynamic blind spot detection and prediction device  402  may also be based upon dangerous situation profiles stored within the dangerous situation history repository  408  within the database  404 . These dangerous situation profiles may be updated and modified over time to improve accuracy association with the detection and prediction capabilities of the dynamic blind spot detection and prediction device  402 . Furthermore, as new dangerous situations are identified, either by actual or near collisions associated with the vehicle  102 , the dynamic blind spot detection and prediction device  402  updates the statistical model  406  and the dangerous situation history repository  408  within the database  404  to include profile information associated with the new dangerous situation. Accordingly, the statistical model  406  and a dangerous situation history repository  408  are modified over time with updated information and the dynamic blind spot detection and prediction device  402  increases its processing capabilities based upon the modifications to the statistical model  406  and the historical information provided by the dangerous history repository  408 . 
     It should be noted that the dynamic blind spot detection and prediction device  402  may be a portable or fixed computing device within the vehicle  102 . The dynamic blind spot detection and prediction device  402  may also be associated with other types of vehicles, such as a plane, train, or other moving vehicle, without departure from the scope of the present subject matter. It should also be noted that the dynamic blind spot detection and prediction device  402  may be any computing device capable of processing information as described above and in more detail below. For example, the dynamic blind spot detection and prediction device  402  may include devices such as a personal computer (e.g., desktop, laptop, palm, etc.) or a handheld device (e.g., cellular telephone, personal digital assistant (PDA), email device, music recording or playback device, etc.), or any other device capable of processing information as described in more detail below. 
       FIG. 5  is a block diagram of an example of an implementation of the dynamic blind spot detection and prediction device  402  that provides the automated dynamic vehicle blind spot determination and dangerous situation prediction within a system, such as the system  400  of  FIG. 4 . A central processing unit (CPU)  500  provides computer instruction execution, computation, and other capabilities within the dynamic blind spot detection and prediction device  402 . A display  502  provides visual information to a user of the dynamic blind spot detection and prediction device  402  and an input device  504  provides input capabilities for the user. 
     The display  502  may include any display device, such as a cathode ray tube (CRT), liquid crystal display (LCD), light emitting diode (LED), projection, touchscreen, or other display element or panel. The input device  504  may include a computer keyboard, a keypad, a mouse, a pen, a joystick, or any other type of input device by which the user may interact with and respond to information on the display  502 . The display  502  and the input device  504  provide user configurability and feedback for operations associated with the dynamic blind spot detection and prediction device  402 . For example, the display  502  may present status associated with the dynamic blind spot detection and prediction device  402 . Additionally, the input device  504  may provide configuration options, such as enabling and disabling, the capabilities of the dynamic blind spot detection and prediction device  402 . Additionally, different profiles may be created for the dynamic blind spot detection and prediction device  402  based upon user preferences, traffic conditions, traffic congestion (e.g., rush hour, country driving, etc.), and many other types of conditions. Accordingly, all such user preferences and/or traffic-based conditions may be created, viewed, modified, edited, or otherwise manipulated by a user of the dynamic blind spot detection and prediction device  402  via the display  502  and the input device  504 . 
     It should be noted that the display  502  and the input device  504  are illustrated with a dashed-line representation within  FIG. 5  to indicate that they are not required components for the dynamic blind spot detection and prediction device  402 . Accordingly, the dynamic blind spot detection and prediction device  402  may operate as a completely automated embedded device without user configurability or feedback. However, the dynamic blind spot detection and prediction device  402  may also provide user configurability and feedback via the display  502  and the input device  504 , respectively. 
     A communication module  506  provides interconnection capabilities that allow the dynamic blind spot detection and prediction device  402  to communicate with other modules within the system  400 , such as the other vehicle_ 1   412  through the other vehicle_N  414 . The communication module  506  may include any electrical, protocol, and protocol conversion capabilities useable to provide the interconnection capabilities. Though the communication module  506  is illustrated as a component-level module for ease of illustration and description purposes, it should be noted that the communication module  506  includes any hardware, programmed processor(s), and memory used to carry out the functions of the communication module  506  as described above and in more detail below. For example, the communication module  506  may include additional controller circuitry in the form of application specific integrated circuits (ASICs), processors, antennas, and/or discrete integrated circuits and components for performing communication and electrical control activities associated with the communication module  506 . Additionally, the communication module  506  also includes interrupt-level, stack-level, and application-level modules as appropriate. Furthermore, the communication module  506  includes any memory components used for storage, execution, and data processing for performing processing activities associated with the communication module  506 . The communication module  506  may also form a portion of other circuitry described without departure from the scope of the present subject matter. 
     A memory  508  includes a data storage area  510 , a code storage area  512 , and a code execution area  514 . The data storage area  510 , the code storage area  512 , and the code execution area  514  store data, code, and provide memory space for code execution, respectively. The memory  508  may be used by any module associated with the dynamic blind spot detection and prediction device  402  and may store and execute instructions executable by the CPU  500  for performing any functions associated with any associated modules, including instructions associated with an operating system and functionality. The CPU  500  executes these instructions to provide the processing capabilities described above and in more detail below for the dynamic blind spot detection and prediction device  402 . 
     It is understood that the memory  508  may include any combination of volatile and non-volatile memory suitable for the intended purpose, distributed or localized as appropriate, and may include other memory segments not illustrated within the present example for ease of illustration purposes. For example, the memory  508  may include a code storage area, a code execution area, and a data area without departure from the scope of the present subject matter. 
     The database  404  is also illustrated within  FIG. 5  and provides storage capabilities for information associated with the automated dynamic vehicle blind spot determination and dangerous situation prediction capabilities of the dynamic blind spot detection and prediction device  402 . It should be noted that, while the example of  FIG. 5  illustrates the database  404  as a separate module, the information stored in association with the database  404  may be alternatively stored within the memory  508  without departure from the scope of the present subject matter. 
     A driver monitoring module  516  provides driver monitoring capabilities for the dynamic blind spot detection and prediction device  402 . A car monitoring module  518  provides vehicle monitoring capabilities for the dynamic blind spot detection and prediction device  402 . A dynamic blind spot determination module  520  provides the analytical capabilities for driver modeling, blind spot determination, and dangerous situation prediction capabilities for the dynamic blind spot detection and prediction device  402 . Each of the driver monitoring module  516 , the car monitoring module  518 , and the dynamic blind spot determination module  520  will be described in more detail in association with  FIG. 6  below. 
     It should be noted that though the driver monitoring module  516 , the car monitoring module  518 , and the dynamic blind spot determination module  520  are illustrated as a component-level modules for ease of illustration and description purposes, it should be noted that each of the driver monitoring module  516 , the car monitoring module  518 , and the dynamic blind spot determination module  520  includes any hardware, programmed processor(s), and memory used to carry out the respective functions of the module as described above and in more detail below. For example, each module may include additional controller circuitry in the form of application specific integrated circuits (ASICs), processors, antennas, and/or discrete integrated circuits and components for performing communication and electrical control activities associated with the respective module. Additionally, each module also includes interrupt-level, stack-level, and application-level modules as appropriate. Furthermore, the each module includes any memory components used for storage, execution, and data processing for performing processing activities associated with the respective module. Each of the driver monitoring module  516 , the car monitoring module  518 , and the dynamic blind spot determination module  520  may also form a portion of other circuitry described without departure from the scope of the present subject matter. 
     The CPU  500 , the display  502 , the input device  504 , the communication module  506 , the memory  508 , the database  404 , the driver monitoring module  516 , the car monitoring module  518 , and the dynamic blind spot determination module  520  are interconnected via an interconnection  522 . The interconnection  522  may include a system bus, a network, or any other interconnection capable of providing the respective components with suitable interconnection for the respective purpose. 
     It should be noted that the dynamic blind spot detection and prediction device  402  is described as a single device for ease of illustration purposes. Such a device may be placed within a single vehicle, such as the vehicle  102  and may communicate predicted and/or actual dangerous conditions to communication receiver devices associated with the vehicle  104  and the vehicle  106 . 
     However, it should be noted that the dynamic blind spot detection and prediction device  402  may also be distributed as a combination of devices. This combination of devices may be distributed among and across vehicles that travel in proximity to one another. For example, the driver monitoring module  516  and the car monitoring module  518  may be located in the vehicle  102  and communicate changes in driver orientation and vehicle information associated with the vehicle  102  to the vehicle  104  and the vehicle  106 . In such a situation, the vehicle  104  and the vehicle  106  may include the dynamic blind spot determination module  520  and calculate changes to blind spots associated with the vehicle  102  and dangerous or potentially dangerous situations without depending upon additional calculations and communications from the vehicle  102 . Additionally, a module similar to the car monitoring module  518  may be located in one or more separate vehicles, such as the vehicle  104  and the vehicle  106 , that communicate information associated with each respective vehicle to the dynamic blind spot determination module  520  located in the vehicle  102 . Many other combinations and distributions of components are possible and all are considered within the scope of the present subject matter. 
     Accordingly, the dynamic blind spot detection and prediction device  402  may take many forms and may be associated with many platforms.  FIG. 7  and  FIG. 8  below describe example processes that may be executed by the dynamic blind spot detection and prediction device  402  to perform the automated dynamic vehicle blind spot determination and dangerous situation prediction associated with the present subject matter. 
       FIG. 6  is a block diagram showing more detail associated with the example driver monitoring module  516 , the car monitoring module  518 , and the dynamic blind spot determination module  520  of the example implementation of the dynamic blind spot detection and prediction device  402  of  FIG. 5 . As can be seen from  FIG. 6 , each of the driver monitoring module  516 , the car monitoring module  518 , and the dynamic blind spot determination module  520  includes several example modules that may be used to perform the respective functions of each module. 
     The driving monitoring module  516  includes a driver detector module  600 , a face detector module  602 , and an eye activity tracker module  604 . The driver detector module  600  includes one or more detectors, such as a camera and/or infrared detector, to detect an orientation of the driver within the vehicle  102 . For purposes of the present description, the orientation of the driver includes aspects of the driver&#39;s orientation, such as a head position of the driver, a body position of the driver, a posture of the driver, and other characteristics of the driver&#39;s orientation within the vehicle  102 . The face detector module  602  utilizes the detected position of the driver determined by the driver detector module  600  to detect the exact face position of the driver. As will be described in more detail below, the face detector module  602  may also receive inputs from modules located within the car monitoring module  518 , such as seat height and/or position information. The eye activity tracker module  604  tracks and eye position of the driver to determine the position, orientation, and direction of the driver&#39;s eyes relative to the determined head and face positions. Based upon this information, the driving monitoring module  516  may determine incident angles of the driver&#39;s eyes upon the rear view mirror, the left side mirror, and the right side mirror. 
     The car monitoring module  518  includes a vehicle type module  606 , a seat height and/or position module  608 , and a vehicle monitoring module  610 . The vehicle type module  606  determines certain characteristics about the vehicle  102 . For example, the vehicle type module  606  determines characteristic associated with the vehicle  102 , such as a vehicle make and model, and associated dimensional characteristics associated with the vehicle  102 . For example, the vehicle type module  606  may provide data and information associated with locations, dimensions, and ranges of motion for adjustability of mirrors, range of adjustability of a driver&#39;s seat, fender shapes and dimensions, and other characteristics associated with the vehicle  102 . 
     The seat height and/or position module  608  determines certain characteristics associated with a driver&#39;s seat within the vehicle  102 . These determinations may be based upon information received from other modules, such as the driver monitoring module  516 . For example, the driver&#39;s seat height and seat position may be determined from the range of adjustability of the driver&#39;s seat. Additionally, an inclination of the driver&#39;s seat may also be determined by the seat height and/or position module  608 . As described above, the seat height and/or position module  608  may provide information to other modules, such as providing a seat height and/or position adjustment to the face detector module  602  located within the driver monitoring module  516 . 
     The vehicle monitoring module  610  determines operational characteristics associated with the vehicle  102 . For example, a speed, steering angle, braking status, engine status, and mirror adjustment positions of the vehicle  102  may all be determined by the vehicle monitoring module  610 . Determination of operational characteristics by the vehicle monitoring module  610  may utilize information received from other modules. For example, a determination of the mirror adjustment positions may utilize information provided by the vehicle type module  606  regarding the range of adjustability of the mirrors within the vehicle  102 . 
     The information generated by the vehicle type module  606 , the seat height and/or position module  608 , and the vehicle monitoring module  610 , the car monitoring module  518  may determine a range of characteristics associated with the vehicle  102 . These characteristics may be provided to other modules within the dynamic vehicle blind spot detection and prediction device  402 , as described above and in more detail below. 
     The dynamic blind spot determination module  520  includes a driver modeling module  612  and a blind spot calculation module  614 . The driver modeling module  612  utilizes average characteristics and ranges of these characteristics for a typical driver of the vehicle  102 . For example, a height or height range of a typical driver, a general direction where a typical driver looks, and other characteristics are used to build an initial model for the driver of the vehicle  102 . The driver modeling module  612  further refines its modeling capabilities using information and data provided by the driver monitoring module  516  and the car monitoring module  518 . The initial and refined models of the driver may be stored within the database  404 , the memory  508 , or within local memory (not shown) within the dynamic blind spot determination module  520 . 
     The driver modeling module  612  also utilizes information and data provided by the driver monitoring module  516  and the car monitoring module  518  to detect a change in the driver&#39;s orientation within the vehicle  102 . The blind spot calculation module  614  receives any detected change in the driver&#39;s orientation and automatically calculates a change to blind spots, such as the left blind spot region  116 , the right blind spot region  118 , and the rear blind spot region  120 , associated with the vehicle  102 . Accordingly, the blind spot calculation module  614  may automatically determine dynamic blind spot changes associated within the vehicle  102 . The automatically calculated change to the blind spots may be based upon changes in the driver&#39;s orientation within the vehicle  102  and may be based, among other things, upon characteristics associated with the vehicle  102  including dimensional characteristics and present operating characteristics. 
       FIG. 7  and  FIG. 8  below illustrate example processing that may be performed in association with the present subject matter and that may be executed by a device, such as the CPU  500  of the dynamic blind spot determination and prediction device  402 . Alternatively, the processes described may be executed by separate processing components within one or more of the driver monitoring module  516 , the car monitoring module  518 , and the dynamic blind spot determination module  520 , as described above and as appropriate. 
       FIG. 7  is a flow chart of an example of an implementation of a process  700  that automatically calculates changes to a blind spot of a vehicle based upon detected changes in a driver&#39;s orientation within the vehicle. At block  702 , the process  700  monitors a driver&#39;s orientation within a vehicle. At block  704 , the process  700  detects a change in the driver&#39;s orientation. At block  706 , the process  700  calculates a change to a blind spot of the vehicle based upon the detected change in the driver&#39;s orientation. 
       FIG. 8  is a flow chart of an example of an implementation of a process  800  that automatically calculates changes to a blind spot of a vehicle by executing a probabilistic model, identifies dangerous and potentially dangerous situations, and alerts other vehicles of any identified dangerous or potentially dangerous situations. At block  802 , the process  800  monitors a driver&#39;s orientation within a vehicle, such as the vehicle  102 . At decision point  804 , the process  800  makes a determination as to whether there has been a change in the driver&#39;s orientation within the vehicle  102 . It should be noted that time out procedures and other error control procedures are not illustrated within the example process  800  for ease of illustration purposes. However, it is understood that all such procedures are considered to be within the scope of the present subject matter for the example process  800 . 
     When a determination is made that there has not been a change in the driver&#39;s orientation within the vehicle  102 , the process  800  returns to block  802  and continues monitoring the driver&#39;s orientation. When a determination is made at decision point  804  that the driver&#39;s orientation has changed, the process  800  determines a characteristic or characteristics of the change in the driver&#39;s orientation at block  806 . For example, characteristics of the change in the driver&#39;s orientation may be obtained from any of the modules described above in association with the driver monitoring module  516 . Any other characteristics that may be associated with a change in orientation of a driver may be used by the process  800  and all are considered within the scope of the present subject matter. 
     At block  808 , the process  800  compares the determined characteristic(s) with known dangerous situations. For example, the process  800  may utilize information stored within the dangerous situation history repository  408  and may compare the change in the determined characteristic(s) with any known dangerous situations or situation profiles stored within the dangerous history repository  408 . At decision point  810 , the process  800  makes a determination as to whether the determined change in the driver&#39;s orientation is associated with any known dangerous situation. When a determination is made that the change in the driver&#39;s orientation is associated with a known dangerous situation, the process  800  alerts the driver of the vehicle  102  at block  812 . The process  800  also alerts drivers of other vehicles in proximity to the vehicle  102  at block  814 . 
     At decision point  816 , the process  800  makes a determination as to whether the danger was realized in the present situation (e.g., a collision) or whether the danger was averted by the alerts generated at blocks  812  and  814 , respectively. When a determination is made the danger was not realized, the process  800  returns to block  802  to monitor the driver&#39;s orientation and to iterate between block  802  and decision point  804  to determine whether the driver&#39;s orientation has changed. 
     When a determination is made at decision point  816  that the danger was realized, such as by a collision or near collision, the process  800  creates and stores a dangerous situation profile to a known dangerous situation database at block  818 . For example, the process  800  may create and store a dangerous situation profile to the dangerous situation history repository  408  within the database  404 . The process  800  returns to block  802  and decision point  804  to iterate as described above. 
     Returning to the description of decision point  810 , when a determination is made at decision point  810  that the change in the driver&#39;s orientation is not associated with the known dangerous situation, the process  800  begins a sequence of probabilistic calculations of potential danger based upon information obtained from the driver monitoring module  516 , the car monitoring module  518 , and the dynamic blind spot determination module  520 , as appropriate, to determine a probability of danger, as described in more detail below. 
     At block  820 , the process  800  determines a probability of potential danger due to the change in the driver&#39;s orientation. At block  822 , the process  800  calculates a change to a blind spot associated with the vehicle  102 . For example, calculating the change to the blind spot includes calculating a change to at least one of a blind spot shape, blind spot dimensions, and a blind spot location. At block  824 , the process  800  determines a probability of potential danger due to the calculated change in the blind spot. 
     At block  826 , the process  800  determines one or more vehicle characteristics associated with the vehicle  102 . For example, the process  800  may determine a size of the vehicle  102 , a mirror adjustment, a speed, a driver&#39;s seat height, a driver&#39;s seat position, and a steering angle of the vehicle  102  as characteristics associated with the vehicle  102 . Additionally, characteristics associated with the vehicle  102  may be obtained from any of the modules described above in association with the car monitoring module  518 . Any other characteristics that may be associated with the vehicle  102  may be used by the process  800  and all are considered within the scope of the present subject matter. At block  828 , the process  800  determines a probability of potential danger due to the determined vehicle characteristic(s). 
     At block  830 , the process determines information about any other vehicle travelling in proximity to the vehicle  102 . For example, characteristics such as those described above with respect to the vehicle  102  may also be collected for other vehicles travelling in proximity to the vehicle  102 . This information may be determined by sensors (not shown) associated with either the vehicle  102  or another vehicle, such as the vehicle  104  and the vehicle  106 . Alternatively, this information may be determined by each of the respective vehicles and communicated to the vehicle at which the process  800  is executed. When this information is collected at a vehicle other than a vehicle that is executing the process  800 , the process  800  may be modified with appropriate request blocks for information and decision points to await responses to requests to be received from the other vehicles. This additional processing is not shown within  FIG. 8  for ease of illustration purposes. However, it is understood that any such communications are considered within the scope of the present subject matter. At block  832 , the process  800  determines a probability of potential danger due to the other vehicles travelling in proximity to the vehicle  102 . 
     At Block  834 , the process  800  runs a statistical model to determine the probability a potential danger based upon the determined probabilities of potential danger due to the change in the driver&#39;s orientation, the potential danger due to the change in the blind spot, the potential danger due to the vehicle  102 &#39;s characteristic(s), and the potential danger due to characteristics associated with any other vehicle travelling in proximity to the vehicle  102 . As such, the statistical model may include a model of at least one of dangerous driver characteristics, dangerous blind spot coordinates, dangerous information about the first vehicle, and dangerous characteristics of an environment surrounding the first vehicle. Further, the statistical model may be any statistical model capable of considering the respective probabilities generated by the process  800 . For example, the statistical model may be any of a variety of probabilistic models, such as a Gaussian, uniform, or other stochastic/statistical model, with a component representing each of the respective probabilities. Additionally, threshold levels for a value may be specified for a result generated by execution of the statistical model, where the threshold may be adjusted based upon preferences and used to trigger alerts as described above and in more detail below. 
     At decision point  836 , the process  800  makes a determination as to whether there is a potential for danger associated with the results generated by execution of the statistical model. When a determination is made at decision point  836  that there is a potential for danger associated with the results generated by execution of the statistical model, the process  800  continues to block  812  and continues processing as described above to generate appropriate alerts. Additionally, the statistical model of dangerous situations may be updated with at least one of a new dangerous driver characteristic, new dangerous blind spot coordinates, new dangerous information about the vehicle  102 , and a new dangerous characteristic of the environment surrounding the vehicle  102 . This updated statistical model information may be stored as either a new dangerous situation profile or an existing dangerous situation profile may be updated within this information within the known dangerous situation database at block  818 , as described above. When a determination is made at decision point  836  that there is not a potential for danger associated with the results generated by execution of this statistical model, the process  800  returns to block  802  and decision point  804  to iterate as described above. 
     Accordingly, the process  800  monitors changes to a driver&#39;s orientation within a vehicle. The process  800  calculates probabilities of potential danger due to changes in the driver&#39;s orientation, due to changes in a blind spot based upon the changes in the driver&#39;s orientation, and due to operational characteristics of the vehicle  102  and any other vehicles travelling in proximity to the vehicle  102 . The process  800  applies a statistical model to the determined probabilities and alerts the driver of the vehicle  102  and any other vehicle travelling in proximity to the vehicle  102  based upon a determined potentially dangerous situation. A threshold may be assigned for triggering of the determination of a potentially dangerous situation. The process  800  also processes and updates dangerous situation information, such as dangerous situation history profiles, within a database of known dangerous situations, such as the dangerous situation history repository  408  stored within the database  404 . Accordingly, the process  800  enhances the dangerous situation history repository  408  over time to improve identification of dangerous situations. 
     Regarding the probabilistic model described in association with  FIG. 8  above, the following is a description of an example of a probabilistic model that may be used by a process, such as the process  800 , to determine a probability of potential danger associated with the present subject. The following equation (1) describes an example probabilistic model. 
       max θ Prob(θ, X,Y,Z )  (1)
 
     Within the example equation (1), the variable “θ” represents a set of coordinates of dangerous blind spots, the variable “X” represents characteristics of a change in orientation of a driver, the variable “Y” represents characteristics of the driver&#39;s vehicle, and the variable “Z” represents characteristics of other vehicles, as described above. 
     The probability calculation of equation (1) may be approximated as a product of functions relevant to various objects by assuming their independence. For example, the following example expression within equation (2) may be used to calculate the variable “X” within equation (1). 
         X =( X   1   ,X   2 )  (2)
 
     Within the example equation (2), the variable “X 1 ” represents information for a head of a driver and the variable “X 2 ” represents information about driver&#39;s eyes. It should be understood that many other variables may be included in the probabilistic expression for equation (1) and any other equation described and that similar expressions for each variable may be created. Based upon such an example expression for the variable “X,” the following example equation (3) may form an example expansion of equation (1). 
       Prob(θ, X,Y,Z )≈Prob(θ,X 1   ,Y,Z )Prob(θ, X   2   ,Y,Z )  (3)
 
     Similar expansions may be performed for other variables. The equation (1) may be modeled as a Gaussian distribution. However, it should be noted that any of a variety of probabilistic or other stochastic/statistical models may be used. The model parameters “θ” may be estimated via monitoring traffic. System data, such as parameters for the variables “X,” “Y,” and “Z” may be collected from monitoring a network of vehicles and detecting traffic accidents. Blind spots found as described may be labeled as dangerous if there were collisions or near collisions during monitoring. 
     As described above in association with  FIGS. 1 through 8 , the example systems and processes provide automatic dynamic vehicle blind spot determination based upon changes in a driver&#39;s orientation within a vehicle, automatic calculation of changes to a blind spot of a vehicle by executing a probabilistic model, automatic identification of dangerous and potentially dangerous situations, and automatic alerts to the driver&#39;s vehicle and other vehicles of any identified dangerous or potentially dangerous situations. It should be understood that the previous description illustrates example approaches to performing the automated dynamic vehicle blind spot determination of the present subject matter. Many other variations and additional activities associated with automatic dynamic vehicle blind spot determination are possible and all are considered within the scope of the present subject matter. 
     Those skilled in the art will recognize, upon consideration of the above teachings, that certain of the above examples are based upon use of a programmed processor such as the CPU  500 . However, the invention is not limited to such exemplary embodiments, since other embodiments could be implemented using hardware component equivalents such as special purpose hardware and/or dedicated processors. Similarly, general purpose computers, microprocessor based computers, micro-controllers, optical computers, analog computers, dedicated processors, application specific circuits and/or dedicated hard wired logic may be used to construct alternative equivalent embodiments. 
     The invention can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment containing both hardware and software elements. In a preferred embodiment, the invention is implemented in software, which includes but is not limited to firmware, resident software, microcode, etc. 
     Furthermore, the invention can take the form of a computer program product accessible from a computer-usable or computer-readable medium providing program code for use by or in connection with a computer or any instruction execution system. For the purposes of this description, a computer-usable or computer readable medium can be any apparatus that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. 
     The medium can be an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system (or apparatus or device) or a propagation medium. Examples of a computer-readable medium include a semiconductor or solid state memory, magnetic tape, a removable computer diskette, a random access memory (RAM), a read-only memory (ROM), a rigid magnetic disk and an optical disk. Current examples of optical disks include compact disk-read only memory (CD-ROM), compact disk-read/write (CD-R/W) and DVD. 
     A data processing system suitable for storing and/or executing program code will include at least one processor coupled directly or indirectly to memory elements through a system bus. The memory elements can include local memory employed during actual execution of the program code, bulk storage, and cache memories which provide temporary storage of at least some program code in order to reduce the number of times code must be retrieved from bulk storage during execution. 
     Input/output or I/O devices (including but not limited to keyboards, displays, pointing devices, etc.) can be coupled to the system either directly or through intervening I/O controllers. 
     Network adapters may also be coupled to the system to enable the data processing system to become coupled to other data processing systems or remote printers or storage devices through intervening private or public networks. Modems, cable modems and Ethernet cards are just a few of the currently available types of network adapters. 
     Those skilled in the art will recognize improvements and modifications to the preferred embodiments of the present invention. All such improvements and modifications are considered within the scope of the concepts disclosed herein and the claims that follow.