Patent Publication Number: US-11398090-B2

Title: Methods and apparatus to generate an augmented environment including a weight indicator for a vehicle

Description:
RELATED APPLICATION 
     This patent arises from a continuation of U.S. patent application Ser. No. 16/191,134, filed on Nov. 14, 2018 and entitled “METHODS AND APPARATUS TO GENERATE AN AUGMENTED ENVIRONMENT INCLUDING A WEIGHT INDICATOR FOR A VEHICLE,” which claims the benefit of U.S. Provisional Patent Application Ser. No. 62/497,317, which was filed on Oct. 15, 2018. U.S. patent application Ser. No. 16/191,134 and U.S. Provisional Patent Application Ser. No. 62/497,317 are incorporated herein in their entirety. 
    
    
     FIELD OF THE DISCLOSURE 
     This disclosure relates generally to vehicle loads and, more particularly, to methods and apparatus to generate an augmented environment including a weight indicator for a vehicle. 
     BACKGROUND 
     All vehicles have a maximum limit on a load the front and rear axles can withstand. In some examples, each axle has a gross axle weight rating (GAWR) that corresponds to the maximum load that may be supported by the axle. Additionally, weight can be poorly distributed on/in the vehicle. If an axle of the vehicle is overloaded or the vehicle is unbalanced, handling degradation, brake problems, and poor headlight aim can occur. In some examples, a vehicle may be misloaded if a particular axle or suspension assembly is bearing a disproportionate amount of the total load on the vehicle. Loading issues can often be relieved by redistributing objects (e.g., cargo, passengers, etc.) to different sections of the vehicle. 
     Mobile devices (e.g., smart phones, headsets, etc.) can now support augmented reality (AR) technology that allows virtual information to augment live video data captured by the mobile device. Augmented reality technology can add and/or remove information from the video data as the video data is presented to user (e.g., by the display of the mobile device). In some examples, AR technology can allow information to be intuitively presented to a user by overlaying relevant virtual information onto video of a physical environment in real-time. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  illustrates an environment of an example vehicle load manager that can be used with an example vehicle with which the examples disclosed herein can be implemented. 
         FIG. 2  is a block diagram depicting the vehicle load condition manager of  FIG. 1 . 
         FIG. 3  is an example illustration of a vehicle and an example augmented reality environment generated by the vehicle load manager  102  of  FIG. 1 . 
         FIG. 4  is another example illustration of a vehicle and an example augmented reality environment generated by the vehicle load manager  102  of  FIG. 1 . 
         FIG. 5  is a flowchart representative of machine readable instructions that may be executed to implement the vehicle load condition manager of  FIG. 1 . 
         FIG. 6  is a block diagram of an example processing platform structured to execute the instructions of  FIG. 5  to implement the vehicle load condition manager of  FIG. 1 . 
     
    
    
     The figures are not to scale. In general, the same reference numbers will be used throughout the drawing(s) and accompanying written description to refer to the same or like parts. 
     DETAILED DESCRIPTION 
     Misloading a vehicle can degrade the reliability of the vehicle. As used herein, the phrase “misloading a vehicle” and all variations thereof, refers to distributing objects on/in a vehicle in such a manner that adversely affects the performance of the vehicle, and can, for example, include exceeding the GAWR of one or both axles, exceeding a weight rating of a suspension assembly, unbalancing a weight distribution associated with the vehicle, etc. In some examples, redistributing the load (passengers, cargo, etc.) on a vehicle can alleviate some or all problems caused by misloading a vehicle. In other examples, removing a load from the vehicle can be required. Traditional means of displaying this information to a user (e.g., a warning light of the dashboard, etc.) may not be intuitive or provide sufficient information for a user to quickly and effectively understand and then correct a loading issue. This lack of intuitiveness or information may lead to a misloaded vehicle. 
     Methods and apparatus disclosed herein combine load data collected by vehicle sensors and live video data to generate an augmented reality environment including the loading condition of a vehicle and weights borne by components of the vehicle. As used herein, the phrase “augmented reality environment” (also referred to herein as an “augmented environment”) is a virtual environment that includes a representation of a physical space (e.g., captured by a video camera) on which computer generated perceptual information is overlaid (e.g., virtual objects are added, physical objects are hidden, etc.). In some examples disclosed herein, objects on/in the vehicle are identified and correlated to load data detected by vehicle sensor(s). In some examples disclosed herein, a map of object shapes, positions, and loads is generated. In some examples disclosed herein, guidance in the form of visual instructions are displayed in the augmented reality environment to indicate how objects can be positioned to properly load the vehicle. 
     In some examples disclosed herein, a mobile device (e.g., a smartphone, a headset, etc.) with a camera can be used to scan a vehicle to determine what objects are on/in the vehicle. In this example, the mobile device can detect a visual anchor on the vehicle to determine the position of identified objects relative to the visual anchor. As used herein, a visual anchor is a visually identifiable feature at a fixed location on a vehicle that can be used to reference the locations of objects in/on the vehicle. In other examples disclosed herein, a camera integral with the vehicle (e.g., a camera mounted above a bed of a truck) can be used to identify an object loaded in a specific area of the vehicle (e.g., a truck bed). In some examples, machine vision techniques can be used to identify objects. In some examples disclosed herein, the augmented reality environment can be displayed on a display integral with the vehicle. In other examples disclosed herein, the augmented reality environment can be presented on a display of the mobile device. 
       FIG. 1  illustrates an environment  100  of an example vehicle load manager  102  that can be used with an example vehicle  104  with which the examples disclosed herein can be implemented. The vehicle  104  includes one or more example wheel and suspension assemblies  105 , one or more example weight sensor(s)  106 , an example trailer hitch  109 , an example trailer weight sensor  110 , and an example camera  122 . In some examples, the vehicle load manager  102  can output information to an example display  114  and/or output information to an example mobile device  120  via an example network  118 . In the illustrated example, the vehicle  104  is a consumer automobile. In other examples, the vehicle  104  can be a commercial truck, a motorcycle, a motorized cart, an all-terrain vehicle, a bus, a motorized scooter, a locomotive, or any other vehicle. 
     The example vehicle load manager  102  enables the generation of an augmented reality environment to guide a user to properly load the vehicle  104 . For example, the vehicle load manager  102  can receive information from sensors (e.g., the weight sensor(s)  106 , the trailer weight sensor  110 , etc.), process the data, and output an augmented reality environment (e.g., to the display  114  or the mobile device  120 ). In some examples, the vehicle load manager  102  can additionally receive live video data from a camera of the mobile device  120  and/or the example camera  122 . In some examples, the vehicle load manager  102  can further generate guidance to be presented to the user to instruct the user how to redistribute the load on the vehicle  104 . The example camera  122  can be, for example, mounted in a center high mounted stop light (CHMSL) of the vehicle (e.g., the brake light indicator above the rear window of a truck bed, etc.). 
     In some examples, one or more of the wheel and suspension assemblies  105  can be coupled via an axle (e.g., a front axle, a rear axle, etc.). Additionally, one or more of the wheel and suspension assemblies  105  can include a weight sensor  106  (e.g., an axle load sensor). In some examples, the weight sensors  106  are ride height sensors that measure the compression of specific ones of the wheel and suspension assemblies  105  (e.g., a deflection of an elastic element of the wheel and suspension assembly  105 ), from which load information can be derived. In other examples, the weight sensors  106  can be transducers capable of converting load information into an electrical signal to be received by the vehicle load manager  102 . 
     In the illustrated example, the vehicle  104  can tow a trailer coupled to the vehicle  104  via the trailer hitch  109 . A trailer may exert a load on the vehicle  104 , which can be measured by the example trailer weight sensor  110 . In some examples, the trailer weight sensor  110  can be integrated into the trailer hitch  109 . In some examples, the trailer weight sensor  110  is a force sensor (e.g., a magnetoelastic sensor, a load cell, a strain gauge, an accelerometer, etc.) capable of measuring forces and/or moments at the trailer hitch  109 . In some examples, the trailer weight sensor  110  measures the load corresponding to the one or more loads exerted on the vehicle  104  by a towed trailer (e.g., total load of the trailer, tongue, etc.). 
     In some examples, the display  114  can present a user of the vehicle  104  with an augmented reality environment produced by the vehicle load manager  102 . In these examples, the display  114  can display an augmented reality environment including one or more instructions, load conditions of the vehicle  104 , and/or weight indications (e.g., how much load is applied to an axle or the wheel and suspension assembly  105 ). 
     In some examples, the vehicle load manager  102  is connected to the network  118 . For example, the network  118  can be a WiFi network or a BlueTooth® network. In other examples, the network  118  can be implemented by any suitable wired and/or wireless network(s) including, for example, one or more data buses, one or more Local Area Networks (LANs), one or more wireless LANs, one or more cellular networks, one or more public networks, etc. The example network  118  enables the example vehicle load manager  102  to be in communication with devices external to the vehicle  104  (e.g., the mobile device  120 ). As used herein, the phrase “in communication,” including variances thereof, encompasses direct communication and/or indirect communication through one or more intermediary components and does not require direct physical (e.g., wired) communication and/or constant communication but, rather, includes selective communication at periodic or aperiodic intervals, as well as one-time events. 
     In the illustrated example of  FIG. 1 , the example mobile device  120  includes a camera and a user interface (e.g., a display). The example mobile device  120  can be one of or a combination of a smart phone, a tablet, a smart watch, a VR/AR headset, smart glasses, etc. In the illustrated example, the mobile device  120  communicates with the vehicle load manager  102  via the network  118 . In other examples, the mobile device  120  can be connected to the vehicle load manager  102  via a wired connection. 
       FIG. 2  is a block diagram depicting the vehicle load manager  102  of  FIG. 1 . The example vehicle load manager  102  includes an example sensor interface  202 , an example load mapper  204 , an example object identifier  206 , an example object-to-weight correlator  208 , an example condition determiner  210 , an example guidance generator  212  and an example augmented reality generator  214 . The vehicle load manager  102  can be implemented fully on the vehicle  104 , fully on the mobile device  120  or any combination thereof. 
     The example sensor interface  202  receives sensor data from the sensors of the example vehicle  104 . For example, the sensor interface  202  can receive input from one or more of the example weight sensors  106  of  FIG. 1 , the example trailer weight sensor  110  of  FIG. 1 , and/or any other sensors (e.g., a fuel level sensor, an engine speed sensor, a vehicle speed sensor, etc.). In some examples, the sensor interface  202  can receive live video data from the mobile device  120 . In some examples, the sensor interface  202  distributes received sensor data to at least one of the load mapper  204 , the object identifier  206 , and/or the augmented reality generator  214 . For example, the sensor interface  202  can distribute load data (e.g., data received from the weight sensors  106 ) to the load mapper  204 . 
     The example load mapper  204  determines a map of the loads on the vehicle  104 . For example, the load mapper  204  can analyze the sensor data distributed by the sensor interface  202  to determine the location and weight of objects on/in the vehicle  104 . For example, the load mapper  204  can analyze the sensor data to determine that an object weighing 85 pounds is placed on the passenger seat of the vehicle  104 . In some examples, the load mapper  204  can generate a visual representation of the vehicle  104  with the additional loads on the vehicle  104 . 
     The example object identifier  206  reviews the data distributed by the sensor interface  202  to determine the location of objects loading the vehicle  104 . For example, the object identifier  206  can analyze live video data from the mobile device  120  and/or the camera  122  to visually identify an object on/in the vehicle  104 . In some examples, the object identifier  206  can identify a visual anchor to create a reference point on the vehicle  104  to reference the location of the identified objects. In other examples, if the camera  122  is fixed to the vehicle  104 , the object identifier  206  can compare the live video data to an image of the vehicle  104  without objects to identify objects in the live video data. In some examples, the object identifier  206  can use machine learning algorithms to identify and locate visual objects. In some examples, the object identifier  206  can use machine vision techniques (e.g., pattern recognition, edge detection, color detection, keypoint mapping, image histogram, etc.). 
     The example object-to-weight correlator  208  correlates the load map generated by the load mapper  204  to the objects identified by the object identifier  206 . For example, the object-to-weight correlator  208  can associate a load in the bed of a vehicle  104  with an object identified by the object identifier  206  in the same location (e.g., tag the identified object with the corresponding load, etc.). In some examples, the object-to-weight correlator  208  can generate a map of shapes, loads, and positions of the object(s) on/in the vehicle  104  based on the load map and identified objects. 
     The example condition determiner  210  analyzes the load map generated by the load mapper  204  and/or sensor data for the sensor interface  202  to determine the load condition of the vehicle  104 . For example, the condition determiner  210  can determine if the load map indicates that the vehicle  104  is overloaded. In other examples, the condition determiner  210  can determine if a GAWR of the vehicle  104  has been exceeded. In other examples, the condition determiner  210  can determine that vehicle  104  is not misloaded. In some examples, the condition determiner  210  can determine whether rearranging the objects on/in the vehicle  104  would alleviate an adverse load condition(s) of the vehicle  104 . 
     The example guidance generator  212  generates instructions to redistribute loads on the vehicle  104  to improve the load condition of the vehicle  104 . For example, the guidance generator  212  can determine that an object in the bed of the vehicle  104  should be moved to a different location in the bed to better distribute the load on the vehicle  104 . In some examples, the guidance generator  212  can generate an instruction that indicates the location and/or direction the object should be moved to correct the loading condition. In some examples, the guidance generator  212  can generate an instruction to guide the user to remove objects on/in the vehicle  104 . In other examples, if the vehicle  104  is properly loaded (e.g., not misloaded), the guidance generator  212  does not generate instructions. In some such examples, the guidance generator  212  can generate an indication that the vehicle  104  is properly loaded. In some examples, the guidance generator  212  can generate instructions even if the vehicle  104  is properly loaded. 
     The example augmented reality generator  214  generates an augmented reality environment based on the data received by the sensor interface  202 , the object-to-weight correlator  208 , and the guidance generator  212 . The example augmented reality generator  214  generates an augmented reality environment to be presented via the display  114  and/or the mobile device  120 . The augmented reality generator  214  can, for example, create a visual indication of the load on each of the wheel and suspension assemblies  105  and/or axles of the vehicle  104 . In some examples, the augmented reality generator  214  can generate a warning if the vehicle  104  is misloaded. In some examples, the augmented reality generator  214  can present a guidance instruction based on the input from the guidance generator  212  (e.g., instructions  314  of  FIG. 3 , the instructions  410  of  FIG. 4 , etc.). In some examples, the augmented reality generator  214  can update the augmented reality environment in real-time as the objects in the vehicle  104  are moved by a user. In other examples, the augmented reality generator  214  can update the generated augmented reality environment periodically at a predetermined interval or in response to a request from a user. 
     While an example manner of implementing the vehicle load manager  102  of  FIG. 1  is illustrated in  FIG. 2 , one or more of the elements, processes, and/or devices illustrated in  FIG. 2  may be combined, divided, re-arranged, omitted, eliminated, and/or implemented in any other way. Further, the example sensor interface  202 , the example load mapper  204 , the example object identifier  206 , the example object-to-weight correlator  208 , the example condition determiner  210 , the example guidance generator  212 , the example augmented reality generator  214 , and/or, more generally, the example vehicle load manager  102  of  FIG. 3  may be implemented by hardware, software, firmware, and/or any combination of hardware, software, and/or firmware. Thus, for example, any of the example sensor interface  202 , the example load mapper  204 , the example object identifier  206 , the example object-to-weight correlator  208 , the example condition determiner  210 , the example guidance generator  212 , the example augmented reality generator  214 , and/or, more generally, the example vehicle load manager  102  could be implemented by one or more analog or digital circuit(s), logic circuits, programmable processor(s), programmable controller(s), graphics processing unit(s) (GPU(s)), digital signal processor(s) (DSP(s)), application specific integrated circuit(s) (ASIC(s)), programmable logic device(s) (PLD(s)), and/or field programmable logic device(s) (FPLD(s)). When reading any of the apparatus or system claims of this patent to cover a purely software and/or firmware implementation, at least one of the example, sensor interface  202 , the example load mapper  204 , the example object identifier  206 , the example object-to-weight correlator  208 , the example condition determiner  210 , the example guidance generator  212 , the example augmented reality generator  214  is/are hereby expressly defined to include a non-transitory computer readable storage device or storage disk such as a memory, a digital versatile disk (DVD), a compact disk (CD), a Blu-ray disk, etc., including the software and/or firmware. Further still, the example vehicle load manager  102  of  FIG. 1  may include one or more elements, processes, and/or devices in addition to, or instead of, those illustrated in  FIG. 2 , and/or may include more than one of any or all of the illustrated elements, processes, and devices. As used herein, the phrase “in communication,” including variations thereof, encompasses direct communication and/or indirect communication through one or more intermediary components, and does not require direct physical (e.g., wired) communication and/or constant communication, but rather additionally includes selective communication at periodic intervals, scheduled intervals, aperiodic intervals, and/or one-time events. 
       FIG. 3  is an example illustration  300  of the vehicle  104  including and an example augmented reality environment generated by the vehicle load manager  102 . The example illustration  300  includes the example vehicle  104  of  FIG. 1 , an example object  302 , and an example visual anchor  304 . The example illustration  300  further includes the example mobile device  120  with an example display  306  displaying an example augmented reality environment  308  generated by the vehicle load manager  102 . The example augmented reality environment  308  includes an example warning  310 , an example front axle weight indicator  312 A, an example rear axle weight indicator  312 B and an example instruction  314 . While an example of the graphical user interface of the augmented reality environment  308  is illustrated in  FIG. 3 , any other suitable graphical user interface may be used to represent the augmented reality environment  308  and/or the output of the vehicle load manager  102 . 
     The example vehicle  104  is loaded by the object  302 . In the illustrated example, the object  302  is loaded in the bed of the vehicle  104 . In other examples, the object  302  may be on/in any other location of the vehicle  104 . In the illustrated example, the load associated with the object  302  exceeds the GAWR of the rear axle of the vehicle  104 , which causes the vehicle  104  to be misloaded. In some examples, the vehicle load manager  102  detects the location, shape, and load associated with the example object  302 . In some examples, a camera associated with the mobile device  120  and/or the camera  122  scans the object  302  such that the vehicle load manager  102  can identify the object  302 . 
     In the illustrated example, a user of the mobile device  120  scans the visual anchor  304  with the mobile device  120  (e.g., captures the visual anchor  304  in the video data generated by the mobile device  120 ) to allow the physical location(s) of the object  302  to be determined by the vehicle load manager  102 . In the illustrated example, the visual anchor  304  is a handle of a front driver door of the vehicle  104 . In other examples, the visual anchor  304  may be any other visually identifiable feature of the vehicle  104  (e.g., a hubcap, the fuel door, etc.). In some examples, the visual anchor  304  may be a sticker and/or other visual feature placed on the vehicle  104  by a user. In some examples, if the visual anchor  304  is not detected by the mobile device  120 , the augmented reality environment  308  can include an instruction to the user to continue scanning the vehicle  104  until the visual anchor  304  is identified by the vehicle load manager  102 . In the illustrated example, the vehicle  104  includes only the visual anchor  304 . In other examples, the vehicle  104  can include any number of anchors in addition to the visual anchor  304 . 
     In the illustrated example, the augmented reality environment  308  is generated based on live video data captured by a camera of the mobile device  120  with the output of the vehicle load manager  102 . That is, as the live video data is presented via the display of the mobile device  120 , the live video data is being augmented by the vehicle load manager  102 . In some examples, the augmented reality environment  308  is updated in real time based on the video data captured by the mobile device  120  and changes to the load condition of the vehicle  104  (e.g., caused by a user adjusting the position of the object  302 , etc.). 
     In the illustrated example of  FIG. 3 , the warning  310  includes the text the “rear axle overloaded,” indicating a GAWR of the rear axle has been exceeded. In some examples, the warning  310  can illustrate the output of the condition determiner  210 . In other examples, the warning  310  may represent any other potentially adverse loading condition(s) of the vehicle  104  (e.g., the front axle is overload, the load is unbalanced, etc.). In other examples where there is no adverse loading condition on the vehicle  104 , the warning  310  may be absent. In this example, the augmented reality environment  308  may further include an indication that the vehicle  104  is properly loaded. 
     In the illustrated example of  FIG. 3 , the front axle weight indicator  312 A is a rectangle underneath the front axle of the vehicle  104  in the augmented reality environment  308  and indicates the front axle is loaded with 2,955 lbs. Similarly, in the illustrated example, the rear axle weight indicator  312 B is a rectangle underneath the rear axle of the vehicle  104  in the augmented reality environment  308  and indicates the rear axle is loaded with 4,630 lbs. In other examples, the front axle weight indicators  312 A and the rear axle weight indicator  312 B can be placed in any suitable location in the augmented reality environment  308  to indicate the load on the front and/or rear axles. In other examples, the front axle weight indicator  312 A and/or the rear axle weight indicator  312 B may include an audio notification to the user. In some examples, each wheel and suspension assembly  105  can have individual weight indicators (e.g., an indicator for the forward driver wheel and suspension assembly  105 , an indicator for the forward passenger wheel and suspension assembly  105 , etc.). 
     In the illustrated example, the instruction  314  includes the text “move load forward” and an arrow pointing to the front of the vehicle  104 . In other examples, the instruction  314  can be in any other suitable location to indicate that the object  302  should be moved forward relative to the vehicle  104 . In some examples, the instruction  314  can include a specific distance and direction to move the object  302 . In some examples, the instruction  314  does not include text. In some examples, the instruction  314  may include any other visual representation to indicate how the load on the vehicle  104  should be redistributed (e.g., a line, a visual representation of the object  302  in the correct location, etc.). In some examples, the instruction  314  may include a non-visual notification to the user (e.g., an audio notification, a vibration, etc.). 
       FIG. 4  is another example illustration  400  of the vehicle  104  and an example augmented reality environment  402  generated by the vehicle load manager  102  of  FIG. 1 . In the illustrated example, the augmented reality environment  402  is displayed via the display  114  of  FIG. 1  and is generated based on the output the vehicle load manager  102  and live video data gathered by the camera  122  of  FIG. 1 . That is, as the live video data is presented via the display of the display  114 , the live video data is being augmented by vehicle load manager  102 . The example vehicle  104  further includes an example bed  403  holding an example first object  406 A and an example second object  406 B. The augmented reality environment  402  includes an example first weight indication  408 A, an example second weight indication  408 B, an example warning  404 , and example instructions  410 . In some examples, the augmented reality environment  402  is updated in real time based on the live video data captured by the camera  122  and changes in the load condition of the bed  403  (e.g., caused by a user adjusting the positions of the first object  406 A and/or the second object  406 B, etc.). 
     In the illustrated example, the first object  406 A is a portable cooler and the second object  406 B is a traffic cone. In other examples, the first object  406 A and the second object  406 B can be any other objects. In some examples, the vehicle load manager  102  of  FIG. 1  can determine the load, shape, and position associated with both the first object  406 A and the second object  406 B. In the illustrated example, the vehicle load manager  102  determines that the vehicle  104  is unbalanced and that the first object  406 A should be moved to properly balance the vehicle  104 . 
     In some examples, the warning  404  can display the output of the condition determiner  210  of  FIG. 2 . In the illustrated example of  FIG. 4 , the warning  404  includes the text “load adjustment recommended,” which indicates the vehicle  104  is unbalanced. In other examples, the warning  404  can indicate any other potentially adverse loading condition(s) of the vehicle  104  (e.g., the front axle is overloaded, etc.). In other examples where there is no adverse loading condition on the vehicle  104 , the warning  404  may not be present in the augmented reality environment  402 . In this example, the augmented reality environment  402  can further display indication that the vehicle  104  is properly loaded. 
     In the illustrated example, the instructions  410  is an arrow pointing to the right with respect to the display  114  including the text “move  6 ” indicating the first object  406 A is to be moved 6 inches to the right on the vehicle  104  to properly balance the vehicle  104 . In other examples, the instructions  410  can be in any suitable location and can include any suitable text and/or visual representation (e.g., a line, a visual representation of the first object  406 A in the correct location, etc.). In some examples, the instructions  410  can include a non-visual notification to the user (e.g., an audio notification, a vibration, etc.). In some examples, the instructions  410  can include multiple steps (e.g., moving both the first object  406 A and the second object  406 B). 
     A flowchart representative of example methods, hardware implemented state machines, and/or any combination thereof for implementing the vehicle load manager  102  of  FIG. 2  is shown in  FIG. 5 . The method can be implemented using machine readable instructions that may be an executable program or portion of an executable program for execution by a computer processor such as the processor  612  shown in the example processor platform  600  discussed below in connection with  FIG. 6 . The program may be embodied in software stored on a non-transitory computer readable storage medium such as a CD-ROM, a floppy disk, a hard drive, a DVD, a Blu-ray disk, or a memory associated with the processor  612 , but the entire program and/or parts thereof could alternatively be executed by a device other than the processor  612  and/or embodied in firmware or dedicated hardware. Further, although the example program is described with reference to the flowchart illustrated in  FIG. 5 , many other methods of implementing the example vehicle load manager  102  may alternatively be used. For example, the order of execution of the blocks may be changed, and/or some of the blocks described may be changed, eliminated, or combined. Additionally or alternatively, any or all of the blocks may be implemented by one or more hardware circuits (e.g., discrete and/or integrated analog and/or digital circuitry, an FPGA, an ASIC, a comparator, an operational-amplifier (op-amp), a logic circuit, etc4.) structured to perform the corresponding operation without executing software or firmware. 
     As mentioned above, the example method of  FIG. 5  may be implemented using executable instructions (e.g., computer and/or machine readable instructions) stored on a non-transitory computer and/or machine readable medium such as a hard disk drive, a flash memory, a read-only memory, a compact disk, a digital versatile disk, a cache, a random-access memory, and/or any other storage device or storage disk in which information is stored for any duration (e.g., for extended time periods, permanently, for brief instances, for temporarily buffering, and/or for caching of the information). As used herein, the term non-transitory computer readable medium is expressly defined to include any type of computer readable storage device and/or storage disk and to exclude propagating signals and to exclude transmission media. 
     “Including” and “comprising” (and all forms and tenses thereof) are used herein to be open ended terms. Thus, whenever a claim employs any form of “include” or “comprise” (e.g., comprises, includes, comprising, including, having, etc.) as a preamble or within a claim recitation of any kind, it is to be understood that additional elements, terms, etc. may be present without falling outside the scope of the corresponding claim or recitation. As used herein, when the phrase “at least” is used as the transition term in, for example, a preamble of a claim, it is open-ended in the same manner as the term “comprising” and “including” are open ended. The term “and/or” when used, for example, in a form such as A, B, and/or C refers to any combination or subset of A, B, C such as (1) A alone, (2) B alone, (3) C alone, (4) A with B, (5) A with C, (6) B with C, and (7) A with B and with C. 
     The method  500  of  FIG. 5  begins at block  502 . At block  502 , if the vehicle load manager  102  is enabled, the method  500  advances to block  504 . If the augmented vehicle load manager  102  is not enabled, the method  500  ends. For example, the vehicle load manager  102  can be enabled by a user. In other examples, the vehicle load manager  102  can automatically become enabled if the vehicle  104  is misloaded. 
     At block  504 , the sensor interface  202  receives load data. For example, the sensor interface  202  can interface with one or more of the weight sensor(s)  106  associated with the wheel and suspension assemblies  105  of the vehicle  104 . In some examples, the sensor interface  202  can further receive load data from the trailer weight sensor  110  and/or any other load sensors of the vehicles (e.g., load sensors associated with the seats of the vehicle  104 ). In some examples, the sensor interface  202  can convert the received load data into a format (e.g., a digital signal, a bit-based value, etc.) processable by the vehicle load manager  102 . In some examples, the sensor interface  202  can distribute the received load data to any other elements of the vehicle load manager  102  (e.g., load mapper  204 , the object identifier  206 , etc.). 
     At block  506 , the sensor interface  202  receives auxiliary sensor data and video data. For example, the sensor interface  202  can receive data from any other sensors on the vehicle  104  necessary to generate a load map of the vehicle (e.g., a fuel level sensor, etc.). In some examples, the sensor interface  202  can convert the received load(s) into a format processable by the vehicle load manager  102 . In some examples, the sensor interface  202  can receive live video data generated by the mobile device  120  and/or the camera  122 . In some examples, the sensor interface  202  can distribute the received auxiliary data and/or live video data to any other components of the vehicle load manager  102  (e.g., load mapper  204 , the object identifier  206 , etc.). In some examples, the live video data captures a visual anchor (e.g., the visual anchor  304  of  FIG. 3 ) and/or one or more objects in/on the vehicle  104 . 
     At block  508 , the load mapper  204  generates a load map of the vehicle  104 . For example, the load mapper  204  can analyze the load data distributed by the sensor interface  202  to generate a map of loads on the vehicle  104 . In some examples, the load mapper  204  can generate a visual representation of the loads on the vehicle  104 . At block  510 , if the object identifier  206  identifies an anchor (e.g., the visual anchor  304  of  FIG. 3 ) on the vehicle  104  captured in the live video data, the method  500  advances to block  514 . If an anchor is not identified by the object identifier  206 , the method  500  advances to block  512 . 
     At block  512 , the object identifier  206  alerts the user to scan an anchor of the vehicle  104 . For example, the object identifier  206  can generate an alert to be displayed (e.g., on a display of the mobile device  120 , the display  114 , etc.). In some examples, the object identifier  206  can augment the live video data to include an indication to scan a visual anchor on the live video data. In some examples, the object identifier  206  can issue a non-visual alert to the user (e.g., vibrating the mobile device, an audible message, etc.). For example, the object identifier  206  may alert the user to reposition the camera generating the live video data to better capture the visual anchor  304 . 
     At block  514 , the object identifier  206  identifies objects in the live video data. For example, the object identifier  206  can process the live video data received by the sensor interface  202  to identify objects on/in the vehicle  104 . In some examples, the object identifier  206  can identify the locations of identified objects relative to the visual anchor  304 . 
     At block  516 , the object-to-weight correlator  208  correlates the detected objects with the load map. For example, the object-to-weight correlator  208  can associated identified objects (e.g., identified by the object identifier  206 ) with the load map (e.g., generated by the load mapper  204 ) in a nearby position. In some examples, the object-to-weight correlator  208  generates a visual map of the load, shape, and position of objects on/in the vehicle  104 . 
     At block  518 , the condition determiner  210  determines if loading guidance is required. For example, the condition determiner  210  can determine if the vehicle  104  is misloaded. In some examples, condition determiner  210  can determine if the vehicle  104  is not optimally loaded. In some examples, the condition determiner  210  can transmit the determined condition to the augmented reality generator  214 . If the condition determiner  210  determines that loading guidance is required, the method  500  advances to block  520 . If the condition determiner  210  determines that loading guidance is not needed, the method  500  advances to block  520 . 
     At block  520 , the guidance generator  212  generates loading guidance. For example, the guidance generator  212  can determine that the objects in and/or on the vehicle  104  should be rearranged to correctly load the vehicle  104 . In some examples, the guidance generator  212  can determine that objects should be removed from the vehicle  104 . In some examples, the guidance generator  212  can indicate the location and distance a specific object in/on the vehicle  104  should be moved to alleviate adverse loading conditions. Additionally or alternatively, the guidance generator  212  can generate a visual representation (e.g., an arrow including text) indicating how one or more objects should be rearranged on the vehicle  104 . 
     At block  522 , the augmented reality generator  214  generates an augmented reality environment. For example, the augmented reality generator  214  can combine the visual map generated by the object-to-weight correlator  208  with the live video data (e.g., presented on the mobile device  120  and/or the camera  122 ). In some examples, the augmented reality generator  214  can generate weight indicators to identify the weight of objects on/in the vehicle  104  (e.g., the weight indicators  408 A and  408 B of  FIG. 4 ). In some examples, the augmented reality generator  214  can generate an indication of a load carried by the front axle or rear axle of the vehicle  104  (e.g., the weight indicators  312 A and  312 B). In some examples, the augmented reality generator  214  can generate an indication of the load carried by each of the wheel and suspension assemblies  105  of  FIG. 1 . 
     At block  524 , the condition determiner  210  determines if additional loading guidance is required. For example, the condition determiner  210  can evaluate a new map generated by the object-to-weight correlator  208  to determine if the vehicle  104  is misloaded. In other examples, the condition determiner can process the live video data to determine if a user has followed the guidance generated by the guidance generator  212 . If the loading condition has been resolved, the method  500  ends. If additional loading guidance is required, the method  500  returns to block  522  to generate new loading guidance. 
       FIG. 6  is a block diagram of an example processor platform  600  capable of executing instructions of  FIG. 5  to implement the vehicle load manager  102  of  FIG. 2 . The processor platform  600  can be, for example, a server, a personal computer, a workstation, a self-learning machine (e.g., a neural network), a mobile device (e.g., a cell phone, a smart phone, a tablet such as an iPad™), a personal digital assistant (PDA), an Internet appliance, a DVD player, a CD player, a digital video recorder, a Blu-ray player, a gaming console, a personal video recorder, a headset or other wearable device, or any other type of computing device. 
     The processor platform  600  of the illustrated example includes a processor  612 . The processor  612  of the illustrated example is hardware. For example, the processor  612  can be implemented by one or more integrated circuits, logic circuits, microprocessors, GPUs, DSPs, or controllers from any desired family or manufacturer. The hardware processor may be a semiconductor based (e.g., silicon based) device. In this example, the processor  612  implements the example sensor interface  202 , the example load mapper  204 , the example object identifier  206 , the example object-to-weight correlator  208 , the example condition determiner  210 , the example guidance generator  212  and the example augmented reality generator  214 . 
     The processor  612  of the illustrated example includes a local memory  613  (e.g., a cache). The processor  612  of the illustrated example is in communication with a main memory including a volatile memory  614  and a non-volatile memory  616  via a bus  618 . The volatile memory  614  may be implemented by Synchronous Dynamic Random Access Memory (SDRAM), Dynamic Random Access Memory (DRAM), RAMBUS® Dynamic Random Access Memory (RDRAM®), and/or any other type of random access memory device. The non-volatile memory  616  may be implemented by flash memory and/or any other desired type of memory device. Access to the main memory  614 ,  616  is controlled by a memory controller. 
     The processor platform  600  of the illustrated example also includes an interface circuit  620 . The interface circuit  620  may be implemented by any type of interface standard, such as an Ethernet interface, a universal serial bus (USB), a Bluetooth® interface, a near field communication (NFC) interface, and/or a PCI express interface. 
     In the illustrated example, one or more input devices  622  are connected to the interface circuit  620 . The input device(s)  622  permit(s) a user to enter data and/or commands into the processor  612 . The input device(s)  622  can be implemented by, for example, an audio sensor, a microphone, a camera (still or video), a keyboard, a button, a mouse, a touchscreen, a track-pad, a trackball, an isopoint device, and/or a voice recognition system. 
     One or more output devices  624  are also connected to the interface circuit  620  of the illustrated example. The output devices  624  can be implemented, for example, by display devices (e.g., a light emitting diode (LED), an organic light emitting diode (OLED), a liquid crystal display (LCD), a cathode ray tube display (CRT), an in-place switching (IPS) display, a touchscreen, etc.), a tactile output device, a printer, and/or speaker. The interface circuit  620  of the illustrated example, thus, typically includes a graphics driver card, a graphics driver chip, and/or a graphics driver processor. 
     The interface circuit  620  of the illustrated example also includes a communication device such as a transmitter, a receiver, a transceiver, a modem, a residential gateway, a wireless access point, and/or a network interface to facilitate exchange of data with external machines (e.g., computing devices of any kind) via a network  626 . The communication can be via, for example, an Ethernet connection, a digital subscriber line (DSL) connection, a telephone line connection, a coaxial cable system, a satellite system, a line-of-site wireless system, a cellular telephone system, etc. 
     The processor platform  600  of the illustrated example also includes one or more mass storage devices  628  for storing software and/or data. Examples of such mass storage devices  628  include floppy disk drives, hard drive disks, compact disk drives, Blu-ray disk drives, redundant array of independent disks (RAID) systems, and digital versatile disk (DVD) drives. 
     The machine executable instructions  632  to implement the methods of  FIG. 5  may be stored in the mass storage device  628 , in the volatile memory  614 , in the non-volatile memory  616 , and/or on a removable non-transitory computer readable storage medium such as a CD or DVD. 
     Example 1 includes an apparatus, comprising a sensor interface to receive load data associated with a vehicle, and receive live video data from a camera, the live video data including a location of an object in the vehicle, a load mapper to generate a map of loads on the vehicle based on the load data, an object-to-weight correlator to correlate a load of the map of loads with the object, and an augmented reality generator to generate an augmented environment identifying the location of the object and the load correlated with the object. 
     Example 2 includes the apparatus of example 1, wherein the augmented environment is updated in real-time. 
     Example 3 includes the apparatus of example 2, wherein the augmented environment is presented to a user via a mobile device, the mobile device including the camera. 
     Example 4 includes the apparatus of example 2, wherein the augmented environment is presented to a user via a display integrated in the vehicle. 
     Example 5 includes the apparatus of example 1, wherein the object-to-weight correlator is further to process the live video data to identify a visual anchor on the vehicle indicating a known location on the vehicle. 
     Example 6 includes the apparatus of example 1 further including a condition determiner to determine a load condition of the vehicle based on the load data, and a guidance generator to modify the augmented environment with a visual indication based on the load condition, the visual indication including an instruction to move the object. 
     Example 7 includes a method, comprising generating a map of loads on a vehicle based on load data associated with a sensor of the vehicle, correlating a load of the map of loads with an object identified using live video data received from a camera, and generating an augmented environment identifying a location of the object and the load correlated with the object. 
     Example 8 includes the method of example 7, the method further including determining a load condition of the vehicle based on the load data and modifying the augmented environment with a visual indication based on the load condition, the visual indication including an instruction to move the object. 
     Example 9 includes the method of example 8, wherein the visual indication is continuously updated as the objected is moved. 
     Example 10 includes the method of example 8, wherein the visual indication is an arrow indicating a direction the object is to be moved. 
     Example 11 includes the method of example 7 further including presenting the augmented environment to a user via a display. 
     Example 12 includes the method of example 11, wherein the display and the camera are included in a mobile device. 
     Example 13 includes the method of example 7 further including identifying a visual anchor indicating a known point on the vehicle. 
     Example 14 includes a non-transitory computer readable medium comprising instructions, which when executed cause a processor to at least generate a map of loads on a vehicle based on load data associated with a sensor of the vehicle, correlate a load of the map of loads with an object, the object identified from live video data received from a camera, and generate an augmented environment identifying a location of the object and the load correlated with the object. 
     Example 15 includes the non-transitory computer readable medium of example 14, further including instructions which when executed cause the processor to determine a load condition of the vehicle based on the load data and modify the augmented environment with a visual indication based on the load condition, the visual indication including an instruction to move the object. 
     Example 16 includes the non-transitory computer readable medium of example 15, wherein the visual indication is continuously updated as the objected is moved. 
     Example 17 includes the non-transitory computer readable medium of example 15, wherein the visual indication is an arrow indicating a direction the object is to be moved. 
     Example 18 includes the non-transitory computer readable medium of example 14, further including instructions which when executed cause a processor to present the augmented environment to a user via a display. 
     Example 19 includes the non-transitory computer readable medium of example 18, wherein the display and the camera are included in a mobile device. 
     Example 20 includes the non-transitory computer readable medium of example 14, further including instructions which when executed cause the processor to identify a visual anchor indicating a known point on the vehicle. 
     Although certain example methods, apparatus, and articles of manufacture have been disclosed herein, the scope of coverage of this patent is not limited thereto. On the contrary, this patent covers all methods, apparatus, and articles of manufacture fairly falling within the scope of the claims of this patent.