Patent Publication Number: US-8990032-B2

Title: In-pavement wireless vibration sensor nodes, networks and systems

Description:
CROSS REFERENCE TO RELATED PATENT APPLICATIONS 
     This application claims priority to Provisional Patent Application No. 61/478,226 filed Apr. 22, 2011, entitled “In-Pavement Wireless Vibration Sensor Nodes, Networks and Systems”, and to Provisional Patent Application No. 61/428,820 filed Dec. 30, 2010 entitled “In-pavement Accelerometer-Based Wireless Sensor Nodes, Networks and Systems and/or Emulating Increased Sample Frequency in a Wireless Sensor Node and/or a Wireless Sensor Network”, both of which are incorporated herein in their entirety. 
    
    
     TECHNICAL FIELD 
     This invention relates to systems that use a wireless sensor network including vibration sensor nodes embedded in pavement. The invention also relates to systems that use vibration readings to generate vehicle parameters that may be used to generate a vehicle classification. The system may also monitor the weight of vehicles and/or their deflection of the pavement while passing over, or near, the sensor node to assess the pavement damage, notify traffic enforcement of traffic violations, tariff fees and/or insurance companies of vehicles they have insured. 
     BACKGROUND OF THE INVENTION 
     Vehicles are typically classified into different categories, such as passenger vehicles, buses and trucks of different sizes. Transportation agencies collect vehicle classifications to plan highway maintenance programs, evaluate highway usage, and optimize the deployment of various resources. There are many classification schemes, but the most common ones use axle counts and the spacing between axles. 
     Transportation agencies measure the weight of vehicles on roads and bridges in order to monitor the state of their repair, enforce weight limits, and charge vehicles fees based on weight criteria. Some agencies use vehicle weight data to predict damage that can be fixed by preservation, which is more cost-effective than rehabilitation. Today, this information is acquired at vehicle weigh stations. To adequately predict the state of repair requires many more weigh stations, which costs too much. 
     There are two basic kinds of weigh stations, static and Weigh In Motion (WIM). Static weigh stations employ bending plates, piezoelectric and load cell sensors to estimate the weight of stopped vehicles. They need substantial space along a road for measurement. The stations are expensive to install and staff. Every vehicle to be weighed must be stopped, wasting valuable time. This stoppage tends to create long queues of vehicles stretching past the station, which poses traffic safety hazards. The vehicles merging back into traffic after being weighed can cause accidents also. 
     WIM stations are replacing static weigh stations. Using the same sensors as static weigh stations, WIM stations estimate axle load while a vehicle is moving at highway speeds. They are also expensive and require frequent calibration as well as concrete pavement installed before and after the station. 
     Some unstaffed WIM stations use a camera to capture the license number or USDOT ID of any vehicle whose WIM measurements suggest it is overweight. These stations, which are referred to as virtual WIM stations, are also expensive and require frequent calibration. 
     SUMMARY OF INVENTION 
     Apparatus and methods are disclosed that may be configured to respond to vibrations in a pavement induced by the travel of a vehicle on the pavement. This summary will start by describing an embedded wireless vibration sensor and how the embedded wireless vibration sensor may be used in a system. The potential component(s) that may be used to make the embedded wireless vibration sensor will be discussed. The embedded wireless vibration sensor can be installed in minutes in any type of pavement (asphalt or concrete). Some of the operational variations will then be mentioned. 
     The embedded wireless vibration sensor node is embedded in pavement and may include at least one vibration sensor and at least a radio transmitter and often a radio transceiver. The embedded wireless vibration sensor node may be configured to operate as follows: The vibration sensor may respond to the vibrations by generating at least one vibration reading. A vibration report may be generated based upon at least one, and often many, of the vibration readings. The radio transmitter may be configured to send the vibration report. The vibrations of the pavement may be generated based upon the movement of the vehicle and its deflection of the pavement near the embedded wireless vibration sensor node. 
     The system may use the vibration report to generate at least one vehicle parameter. The vehicle parameter may include a length estimate, an axle count estimate, an axle position estimate vector, an axle spacing vector and/or an axle width estimate. In certain implementations, the vehicle parameter may include each of these components. The vehicle parameters may be used to generate a vehicle classification for the vehicle. 
     The system may use the vibration report to generate a weight estimate of the vehicle and/or a deflection estimate of the vehicle acting on the pavement. In some implementations, a movement estimate and/or the vehicle parameters may be used to further support generating the weight estimate and/or the deflection estimate. 
     A vehicle identification may be used with the vehicle classification and the weight estimate and/or the deflection estimate, as well as possibly the vehicle parameters and the movement estimate, to generate a vehicle travel record. The vehicle travel record may also include the vehicle classification, as well as possibly a time stamp. 
     The vehicle travel record may be used to generate a traffic ticket message, and/or a tariff message, and/or an insurance message, for the vehicle. These messages may include much the same information, but may differ in terms of when they are generated and whom they are sent to. The traffic ticket message may only be generated when the vehicle is breaking a traffic regulation. The tariff message may be sent for all vehicles in certain vehicle classifications and/or exceeding a certain weight threshold and/or a deflection threshold. The insurance message may only be generated for vehicles whose vehicle identifications indicate that an insurance company has agreed to pay for the insurance message about the vehicle. 
     The embedded wireless vibration sensor node may be built from any of several components, in particular, a vibration sensor module, a wireless vibration sensor, and/or a wireless vibration sensor node.
         The vibration sensor module may include at least one vibration sensor configured to respond to the vibrations in the pavement to create at least one vibration reading.   The wireless vibration sensor may include the vibration sensor and a radio transmitter configured to send the vibration report based upon the vibration reading.   The wireless vibration sensor node may be configured for embedding in the pavement and may include the vibration sensor and the radio transmitter and/or transceiver.       

     The apparatus may further include at least one of the following processors:
         A first processor configured to respond to the vibration readings to generate the vibration report.   A second processor configured to respond to the vibration report to generate at least one vehicle parameter.   A third processor configured to respond to the vehicle parameter of the vehicle to generate the vehicle classification.   A fourth processor configured to respond to the vibration report to generate the weight estimate and/or the deflection estimate.   A fifth processor configured to respond to the vehicle classification, a vehicle identification, a vehicle movement estimate, the weight estimate and/or the deflection estimate to generate a vehicle travel record.   And a sixth processor configured to respond to the vehicle travel record to generate the traffic ticket message, the tariff message and/or the insurance message.       

     An access point may be configured to wirelessly communicate with at least one of the embedded wireless vibration sensor nodes to receive the vibration reports. Various combinations of the second through the sixth processor may be implemented in the access point. In some implementations, the embedded wireless vibration sensor node may implement some of the processors. 
     These processors individually and/or collectively may be implemented as one or more instances of a processor-unit that may include a finite state machine, a computer coupled to a memory containing a program system, an inferential engine and/or a neural network. The apparatus may further include a computer readable memory, a disk drive and/or a server, each configured to deliver the program system and/or an installation package to the processor-unit to implement at least part of the disclosed method and/or apparatus. These delivery mechanisms may be controlled by an entity directing and/or benefiting from the delivery to the processor-unit, irrespective of where the server may be located, or the computer readable memory or disk drive was written. 
     The disclosed method may include steps initializing at least one of the disclosed apparatus, and/or operating at least one of the apparatus and/or using at least one of the apparatus to create any combination of the vibration report, the vehicle parameter, the vehicle classification, the vehicle travel record, the traffic ticket message, the tariff message and/or the insurance message. The method may produce any of the vibration report, the vehicle parameter, the vehicle classification, the vehicle travel record, the traffic ticket message, the tariff message and/or the insurance message. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  shows an example system operating and/or using a wireless sensor network that may include at least one access point configured to wirelessly communicate with at least one embedded wireless vibration sensor node embedded in pavement with a vehicle traveling on the pavement inducing vibrations by the deflection of the pavement. The access point receives a vibration report in response to the vibration readings of the vehicle traveling on the pavement. The system may further produce at least one vehicle parameter, a vehicle classification, a vehicle travel record, a traffic ticket message, a tariff message and/or an insurance message. 
         FIGS. 2A and 2B  show examples of how the vehicle parameters may be alternatively defined by different implementations of the system and its components of  FIG. 1 . 
         FIGS. 3A and 3B  show examples of how the system and its components of  FIG. 1  may implement and/or use the vehicle parameter. 
         FIG. 3C  shows some details of certain implementations of the weight estimate. 
         FIG. 4  shows some example implementations of components that may be used and/or included in the embedded wireless vibration sensor node embedded in the pavement shown in  FIG. 1 . 
         FIG. 5  shows an example of the embedded wireless vibration sensor node further including the second processor and the fourth processor, with the vibration report further indicating the vehicle parameter and the vehicle classification. 
         FIGS. 6 and 7  show examples of various combinations of the second through the sixth processor may be implemented in the access point. 
         FIG. 8A  shows an example of the system of  FIG. 1  further including more than one instances of the embedded wireless vibration sensor nodes embedded in the pavement of a lane of a roadway. The system may further include one or more wireless magnetic sensor node also embedded in the pavement. 
         FIGS. 8B and 8C  show some other examples of the system of  FIGS. 1 and 8A  that may also determine the axle width for a vehicle with two axles, as well as possibly further include radar, infrared sensors and/or optical sensors. The system may also include a temperature sensor that may or may not be implemented in the embedded wireless vibration sensor nodes. 
         FIG. 9  shows the processors may be individually and/or collectively may be implemented as one or more instances of a processor-unit. The apparatus may further include delivery mechanisms that may be controlled by an entity directing and/or benefiting from the delivery to the processor-unit of the program system and/or an installation package to implement at least part of the disclosed method and/or apparatus. 
         FIGS. 10 to 14  show some details of the program system of  FIG. 9  that may serve as examples for at least some of the steps of the disclosed method. 
     
    
    
     DETAILED DESCRIPTION OF DRAWINGS 
     This invention relates to systems that use a wireless sensor network including vibration sensor nodes embedded in pavement. The invention also relates to systems that use vibration readings to generate vehicle parameters such as vehicle length, the number, positions and/or spacing of some or all of the axles of the vehicle, which may be used to generate a vehicle classification. The system may also monitor the weight of vehicles passing over or near them on a lane to assess the pavement damage of the lane. 
     This invention relates to wireless weigh-in-motion or W-WIM systems and their components, in particular, to wireless sensor nodes configured to operate one or more vibration sensors, access points configured to wirelessly communicate with the one or more wireless sensor nodes, and processors configured to use vibration readings of the wireless sensor nodes to generate the vehicle parameters and/or the vehicle classification and/or an estimated weight of the vehicle and/or the deflection of the pavement caused by the passage of the vehicle. 
     Referring more specifically to the Figures,  FIG. 1  shows an example system  10  that may include at least one wireless sensor network  94 . The wireless sensor network  94  may include at least one access point  90  configured to wirelessly communicate  92  with at least one embedded wireless vibration sensor node  49  embedded in pavement  8  with a vehicle  6  traveling  20  on the pavement inducing vibrations  34  in the pavement due to the deflection  31  of the pavement. An access point  90  receives a vibration report  70  via wireless communication  92  from the wireless vibration sensor node  49  in response to the vibrations  34  of the vehicle  6  traveling  20  on the pavement  8 . 
     The pavement  8  may include a filler  8 F and a bonding agent  8 B. The filler  8 F may include sand, gravel and/or pumice. The bonding agent  8 B may include asphalt and/or cement. 
     The embedded wireless vibration sensor node  49  may include at least one vibration sensor  60  and at least a radio transmitter  82  and often a radio transceiver  80  as shown. The embedded wireless vibration sensor node  49  may be configured to operate as follows: The vibration sensor  60  may respond to the vibrations  34  by generating at least one vibration reading  62 . The vibration report  70  may be generated based upon at least one and often many vibration readings  62 . The radio transmitter  82  may be configured to send the vibration report  62 . 
     The system  10  may use the vibration report  70  to generate at least one vehicle parameter  200  of the vehicle  6 . The vehicle parameter  200  may include a length estimate  202 , an axle count estimate  204 , an axle spacing vector  206 , and/or an axle width estimate  207 . In certain implementations, the vehicle parameter  200  may include each of these components. 
     For the sake of simplifying the discussion, most of this document will focus on the vehicle parameter  200  including each of the components  202 ,  204 ,  206  and  207 . This should not be interpreted as intending to limit the scope of the claims. By way of example, consider the following interpretation of the vehicle parameter  200  for the vehicle  6  shown in  FIG. 1 .
         The length estimate  202  may approximate the vehicle length  30 .   The axle count estimate  204  may be three, representing the count of the first axle  21 , the second axle  22  and the third axle  23 .   The axle spacing vector  206  may have more than one coordinate components. For example, for a vehicle  6  including three axles  21 ,  22  and  23 , the axle spacing vector  206  may approximate a first to second axle spacing  50 , the second to third axle spacing  52 . The first to second spacing  50  may approximate the spacing between the first axle  21  and the second axle  22 . The second to third spacing  52  may approximate the spacing between the second axle  22  and the third axle  23 . Note that the order of these components may differ from one implementation to another, and that the units may vary, from meters, to centimeters, to feet, and/or to inches in some implementations.   The wheel base estimate  207  may approximate the axle width  24  of the vehicle  6 . The units may vary, from meters, to centimeters, to feet, and/or to inches in some implementations. Alternatively, the wheel base estimate  207  may indicate one of several ranges, for instance, less than six feet, between six feet and ten feet, between 10 and 15 feet, between 15 feet and twenty feet and/or greater than twenty feet.   The wheel base estimate  207  may be specifically used when the axle count estimate  204  indicates a vehicle with two axles to classify motor cycles, pickups, trucks and busses. In some implementations, the wheel base estimate  207  may only be occur in the vehicle parameters  200  when the axle count estimate  204  indicates two axles.   The generation of the vehicle parameters  200  will be further discussed later.       

     The vehicle parameters  200 , in some situations, the length estimate  202 , the axle count estimate  204 , the axle spacing vector  206  and the wheel base estimate  207  may be used to generate a vehicle classification  220  for the vehicle  6 . In this example, the vehicle classification may indicate a vehicle capable of carrying a standard size container of roughly  40  feet (thirteen meters) in length. 
     The system  10  may use the vibration report  70  to generate a weight estimate  210  of the vehicle  6  and/or to generate a deflection estimate  212  of the pavement  8  in response to the travel  20  of the vehicle  6  over the pavement.
         The weight estimate  210  may be in terms of different units in different implementations, for instance, units of pounds, tons, kilograms and/or metric tons are four reasonable choices that may be found in various implementations of the system  10  somewhere on the planet.   Similarly, the deflection estimate  212  may be may be in terms of different units in different implementations.   In some implementations, a movement estimate  22  and/or the vehicle parameters  200 ,  202 ,  204 ,  206  and/or  207  may be used to further support generating the weight estimate  210 .   The generation of the weight estimate  210  and/or the deflection estimate  212  will be discussed in detail later.   The movement estimate  22  may indicate at least a velocity of the vehicle  6  and preferably also indicating its acceleration. Alternatively, the movement estimate  22  may be in terms of time to travel  20  between two of the embedded wireless vibration sensor nodes  49 .       

     The vehicle identification  232  for the vehicle  6  may be used with the vehicle classification  220  and the weight estimate  210 , as well as possibly the vehicle parameters  200 - 206  and the movement estimate  22  to generate a vehicle travel record  230 . In some implementations, the vehicle travel record  230  may also include the vehicle classification  220 , the weight estimate  210 , the vehicle parameters  200 - 207  and/or the movement estimate  22 , as well as possibly a time stamp  234 . In some implementations, the vehicle travel record  230  may include a compression of some or all of these components. For instance, if the vehicle identification  232  is an image of a license plate of the vehicle  6 , it may be a compressed image using some compression technology such as JPEG. 
     The system  10  may use the vehicle travel record  230  to generate at least one of a traffic ticket message  250 , a tariff message  252  and/or an insurance message  254 , each for the vehicle  6 . Consider the following examples of these generated products of the process of operating the system:
         These messages  250 ,  252  and  254  may include much the same information, but may differ in terms of when they are generated and whom they are sent to.   For example, the traffic ticket message  250  may indicate that the vehicle  6  with three axles  21 ,  22 , and  23  with the approximate vehicle length  30  of 55 feet and carrying a vehicle weight  32  of approximately 120 tons has a movement estimate  22  of about 80 miles per hour with a confidence interval within 2 miles per hour. The vehicle  6  may be identified  232  by an image of its license plate and/or a Radio Frequency IDentification (RF-ID) tag.   The traffic ticket message  250  may only be generated when the vehicle  6  is breaking a traffic regulation. The tariff message  252  may be sent for all vehicles  6  in certain vehicle classifications  220 . The insurance message  254  may only be generated for vehicles  6  whose vehicle identifications  232  indicate that an insurance company has agreed to pay for the insurance message about the vehicle  6 .       

     Several processors  100 ,  102 ,  104 ,  106 ,  108 , and/or  110  may be involved in the data processing regarding these vibration reports  70  in various implementations of the system  10 .
         A first processor  100  may be configured to respond to the vibration readings  62  to generate the vibration report  70 .   A second processor  102  may be configured to respond to the vibration report  70  to generate at least part of the vehicle parameter  200  of the vehicle  6 .   A third processor  104  may be configured to respond to the vehicle parameter  200  of the vehicle  6  to generate the vehicle classification  220 .   A fourth processor  106  may be configured to respond to the vibration report  70  to generate the weight estimate  210  of the vehicle weight  32  and/or the deflection estimate  212  of the deflection  31  of the pavement  8  from the vehicle  6  traveling  20  over the pavement.   A fifth processor  108  may be configured to respond to the vehicle classification  220 , the weight estimate  210 , the vehicle identification  232  and the vehicle movement estimate  22  to generate the vehicle travel record  230  for the vehicle  6 .   And a sixth processor  110  may be configured to respond to the vehicle travel record  230  to generate at least one of the traffic ticket message  250 , the tariff message  252  and the insurance message  254 .       

     The wireless sensor network  94 , the transmitter  82  and/or the transceiver  80  at the wireless sensor nodes  49  may be configured to operate in accord with a wireless communication  92  protocol, such as at least one version of an Institute for Electrical and Electronic Engineering (IEEE) 802.15.4 protocol, an IEEE 802.11 protocol, a Bluetooth protocol and/or a Bluetooth low power protocol. 
     The wireless sensor network  94  may use wireless communications  92  employing a modulation-demodulation scheme, that may include any combination of a frequency division multiple access scheme, a Time Division Multiple Access (TDMA) scheme, a Code Division Multiple Access (CDMA) scheme, a frequency hopping scheme, a time hopping scheme, and/or an Orthogonal Frequency Division Multiplexing (OFDM) scheme. 
       FIGS. 2A and 2B  show examples of how the vehicle parameters  200  may be alternatively defined by different implementations of the system and its components of  FIG. 1 .
           FIG. 2A  shows the vehicle length  30  defined and measured as the distance between the front and the back of the vehicle  6 . The first axle  21  is shown with a first axle position  54  as measured from the back of the vehicle  6 . The second axle  22  is shown with a second axle position  56  measured again from the back of the vehicle  6 . And the third axle  23  is shown with a third axle position  58  also measured from the back of the vehicle  6 .     FIG. 2B  shows the vehicle length  30  defined and measured as the distance between the first axle  21  and the last, in this case, the third axle  23 .   The axle positions are measured in this example from the first axle, so the first axle position  54  is always zero, and may not be reported. The second axle position  56  is the spacing between the first axle  21  and the second axle  22 . The third axle position  58  is the distance from the first axle  21  to the third axle  23 , which may be seen as the sum of the first to second spacing  50  and the second to third spacing  52  of  FIG. 1 .       

       FIGS. 3A and 3B  show examples of how the system  10  and its processors  100 ,  102 ,  104 ,  106 ,  108 , and/or  110  of  FIG. 1  may implement and/or use the vehicle parameter  200 .
         As used herein, the axle count estimate  204  may represent the number of axles as essentially an integer, possibly with a designator for a fifth wheel that may not be considered as a full axle.     FIG. 3A  shows an example of the vehicle parameters  200  including an axle count estimate  204  and an axle position estimate vector  208 , which could be based upon the definitions and measurements shown in  FIG. 2A  and/or  FIG. 2B .     FIG. 3B  shows another example of the vehicle parameters  200  including the length estimate  202 , the axle count estimate  204 , the axle spacing vector  206  and/or the axle position estimate vector  208 .   The length estimate  202  may be based upon the definitions and measurements of the vehicle length  30  as shown in  FIGS. 1 and 2B  or in  FIG. 2A .   The axle spacing vector  206  may represent the spacing between at least some of the adjacent axles.  FIG. 1  shows the first to second spacing  50  as the distance between the first axle  21  and the second axle  22 . The second to third spacing  52  as the distance between the second axle  22  and the third axle  23 .   Note that in some implementations, vehicle classification may not require knowing all the spacing estimates between axles. By way of example, in the United States, when the axle count estimate  204  has a value of 5, the spacing between the third axle and the fourth axle is not used in classifying the vehicle  6 , and may not be generated.   The axle position estimate  208  may be based upon the definitions and measurements shown in  FIG. 2A  and/or  FIG. 2B .       

       FIG. 3C  shows some details of certain implementations of the weight estimate  210 , which may contain a static weight estimate  214  and a dynamic weight component  216 . The static weight estimate  214  may refer to the weight of the vehicle  6 , possibly as measured for a specific axle, such as the first axle  21 . The dynamic weight component  216  may refer to the force induced by the vehicle  6 , possibly from the oscillation or vibration of the axles and/or the chassis of the vehicle. 
     While there is more to discuss about how the system  10  operates,  FIG. 4  will discuss how the embedded wireless vibration sensor node  49  is created in the pavement  8 . 
       FIG. 4  shows some example implementations of components that may be used and/or included in the embedded wireless vibration sensor node  49  embedded in the pavement shown in  FIG. 1 . 
     The vibration sensor  60  may include an analog vibration sensor  64  configured to generate an analog vibration signal  65  presented to an analog to digital converter  66  that may generate the vibration reading  62  in response to the stimulus provided by the analog vibration signal.
         In some embodiments the vibration reading  62  may represent a number, which may typically be in a fixed point format or a floating point numeric format.   The vibration sensor  60  may in some situations further include an amplifier to further stimulate the analog to digital converter  66 .   The analog vibration sensor  64  may be implemented with a MEMS vibration sensor  45 , which has also been called a MEMS accelerometer in the cited provisional patent application. As used herein, MEMS stands for Micro-Electro-Mechanical Systems.   In some embodiments, the analog vibration sensor  64  may be implemented by at least one Piezoelectric (PZ) vibration sensor  44 .       

     Among the other components that may be included or used to create the embedded wireless vibration sensor node  49 , are a vibration sensor module  46 , a wireless vibration sensor  47  and/or a wireless sensor node  43 .
         The vibration sensor module  46  may include at least one of the vibration sensors  60  possibly coupled to a printed circuit board or insertion package configured for installation into the wireless vibration sensor  48  and/or the wireless vibration sensor node  43 .   The wireless vibration sensor  47  may include the vibration sensor  60  and a radio transmitter  82  and/or a transceiver  80  configured to send the vibration report  70  based upon the vibration reading  62 .       

     The wireless vibration sensor node  43  may be configured to be embedded in the pavement  8  and may include the vibration sensor  60  and the radio transmitter  82  and/or transceiver  80 .
         The wireless vibration sensor node  43  may further include the vibration sensor  60  communicatively coupled to send the vibration readings  62  to the first processor  100 , which in turn may communicate the vibration report  70  to the radio transmitter  82  and/or the transceiver  80 .   While not shown in the Figures, the wireless vibration sensor node  43  may further include a power controller that may use a battery to power the other active components. A photocell and/or strain gauge may be used to recharge the battery.   In some implementations, at least one of the embedded wireless vibration sensors  47 , the wireless vibration sensor node  43  and/or the embedded wireless vibration sensor node  49  may include a temperature sensor  68  configured to generate a temperature reading  69 . The first processor  100  may be further configured to generate and send a temperature report  74 , possibly as part of a sensor message  72 . More than one of the sensor messages  72  may be used to send the vibration report  70  and/or the temperature report  74 .   These components may be enclosed in an embedding package  42  by a cover  41 . The embedding package  42  may be filled with a packing material to minimize mechanical shock. The cover  41  may be screwed down onto the embedding package, possibly with a strip of elastomer sealant or glue to further bind the cover  41  to the embedding package  42 . The embedding package  42  may approximate a cube about 3 inches on a side in some implementations.   The wireless vibration sensor node  43  may include a means for suppressing  39  acoustic noise affecting the vibration sensor  60  from the engines of the vehicles  6  passing the embedded wireless sensor node  49 . The means for suppressing may includes the segment of pavement in which the wireless sensor node  43  is embedded, the fused silica packing in the wireless sensor node and/or an air-tight seal between the embedding package  42  and the cover  41 .       

     As used herein, providing a component to create something refers to placing that component in position and then creating that something. This may use an automated or human parts assembly process.
         The MEMS vibration sensor  45  and/or the Piezoelectric vibration sensor may be provided to create the vibration sensor  60 .   The vibration sensor  60  may be provided to create the vibration sensor module  46 , the wireless vibration sensor  47 , the wireless vibration sensor node  43  and/or the embedded wireless vibrations sensor node  49 .   The vibration sensor module  46  may be provided to create the wireless vibration sensor  47 , the wireless vibration sensor node  43  and/or the embedded wireless vibrations sensor node  49 .   The wireless vibration sensor  47  may be provided to create the wireless vibration sensor node  43  and/or the embedded wireless vibrations sensor node  49 .   And the wireless vibration sensor node  43  may be provided into a cavity in the pavement  8  to create the embedded wireless vibrations sensor node  49 . The wireless vibration sensor node  43  may be placed into a four inch hole drilled into the pavement  8  that is then filled with epoxy to create the embedded wireless vibrations sensor node  49 . Installation of the embedded wireless vibration sensor node may take under ten minutes.       

     In some implementations, the embedded wireless vibration sensor node may implement some of the processors. 
       FIG. 5  shows an example of the embedded wireless vibration sensor node  49  further including the second processor  102  and the third processor  104 , with the vibration report  70  further indicating the vehicle parameter  200  and the vehicle classification  220 . 
       FIGS. 6 and 7  show examples of various combinations of the second through the sixth processor  102  to  110  may be implemented in the access point  90 .
           FIG. 6  shows the access point  90  may include the second processor  102  and the fourth processor  106 .     FIG. 7  shows the access point  90  may further include the third processor  104 , the fifth processor  108  and the sixth processor  110 .       

     The wireless sensor network  94  may also include wireless sensor nodes  96  operating a magnetic sensor  97 , an optical sensor, a digital camera, and/or a radar. 
       FIG. 8A to 8C  show examples of some of the details of the system  10  of  FIG. 1 . 
       FIG. 8A  shows an example of the system  10  of  FIG. 1  further including more than one, in this case four instances of the embedded wireless vibration sensor nodes  49  to  49 - 4  embedded in the pavement  8  of a lane  2  of a roadway. The system  10  may further include one or more, in this case two instances, of a wireless magnetic sensor node  96  and  96 - 2  embedded in the pavement  8  of the lane  2 . The system  10  may be configured to use the wireless magnetic sensor nodes  96  and  96 - 2  to generate the movement estimate  22  of the vehicle  6  traveling  20  in the lane  2 . In some embodiments, the wireless magnetic sensor nodes  96  and  96 - 2  may be used to generate and/or refine the length estimate  202 . 
     The wireless magnetic sensor node  96  may include a magnetic sensor  97  configured to generate magnetic readings  98  as the vehicle  6  travels  20  close to the node  96 . These magnetic readings  98  may be used to generate a magnetic report  99  that may be sent by the transmitter  82  to the access point  90  for use in generating the movement estimate  22  and/or the length estimate  202 . 
       FIG. 8B  shows another example of the system of  FIGS. 1 and 8A  that may also determine the axle width  24  for a vehicle  6  with two axles. This example of the system  10  includes three columns of the wireless vibration sensor nodes configured with a distance  25  between the columns. The first column may include the wireless vibrations sensor nodes  49  to  49 - 4 . The second column may include the wireless vibration sensor nodes  49 - 5  to  49 - 8 . The third column may include the wireless vibration sensor nodes  49 - 9  to  49 - 12 . 
     The distance  25  may be measured in different fashions, such as from one edge as shown in  FIG. 8B , or from the centers as shown in  FIG. 8C . 
     The columns may have the same number of wireless vibration sensor nodes as shown in  FIG. 8B  or may have different numbers of wireless vibration sensor nodes as shown in  FIG. 8C . 
     In some embodiments, more than two columns may be useful in fourth processing  106  the vibration readings  62  and/or the vibration reports  70  to generate the weight estimate  210 . Consider the following example implementations:
         The static weight estimate  214  may be generated by removing the dynamic weight component  216  from the weight estimate  210 . This removal may be performed by averaging the weight estimates based upon each of the columns of embedded wireless vibration sensor nodes  49  and so on. Other signal processing steps may be used to remove the dynamic weight component  216  from the weight estimate  210 . This may be preferred when the distance  25  between the columns is at least about twelve feet or at least about four meters. Such implementations of the system  10  may use the weight estimate  210  as the static weight estimate  214  after the dynamic weight component  216  has been removed.   The dynamic weight component  216  may be recognized in the weight estimate  210  thereby revealing the static weight estimate  214 , which may be calculated later. The system  10  may be implemented to use the weight estimate  210  with the recognized dynamic weight component  216 .   Note that in some implementations of the system  10 , combinations of these last two examples may be found.       

       FIG. 8C  shows another example of the system  10  of  FIGS. 1 and 8A  that may further include a radar  59 , an infrared sensor  57  and/or optical sensors  61 . The system  10  may also include a temperature sensor  68  that may not be implemented in the embedded wireless vibration sensor nodes  49 . The distance  25  may be measured from the centers. The columns may have different numbers of wireless vibration sensor nodes. For example, the first column may include three wireless vibration sensor nodes  49 ,  49 - 2  and  49 - 4 , whereas the second column may include four wireless vibration sensor nodes  49 - 5  to  49 - 8 . The columns may not be arranged perpendicular to the travel  20  of the vehicle  6 , as shown in this Figure.
         The radar  59  may be used to at least partly determine the movement estimate  22 . In other embodiments, the movement estimate  22  may be at least partly determined by the columns of wireless vibration sensors  49  to  49 - 8  and the distance  25  between the columns. The infrared sensor  57  may also be used to at least partly determine the movement estimate  232 .   The Radio Frequency Identification (RF-ID) sensor  63  may be configured to respond to a RF-ID tag to at least partly generate the vehicle identification  232 . For example, an insurance carrier may require the installation of the RF-ID tag so that the vehicles  6  it insures may be tracked.   An optical sensor  61  may respond to a license plate on the vehicle  6  to at least partly generate the vehicle identification  232 .   The access point  90  may be configured to communicate with any combination of the infrared sensor  57 , the radar  59 , the optical sensor  61 , the RF ID sensor  63  and/or the temperature sensor  68 , either through the use of a wireless communication  94  as previously discussed or a wireline communication  95 . As used herein, a wireline communication  95  uses at least one wireline physical transport. Examples of wireline physical transports include, but are not limited to, one or more conductive wires and/or fiber optical conduits.   The access point  90  may use an internal clock and/or an external clock to generate a time stamp  234 .       

       FIG. 9  shows the processors  100  to  110  may be individually and/or collectively may be implemented as one or more instances of a processor-unit  120  that may include a finite state machine  150 , a computer  152  coupled  156  to a memory  154  containing a program system  300 , an inferential engine  158  and/or a neural network  160 . The apparatus may further include examples of a delivery mechanism  230 , which may include a computer readable memory  222 , a disk drive  224  and/or a server  226 , each configured to deliver  228  the program system  300  and/or an installation package  209  to the processor-unit  120  to implement at least part of the disclosed method and/or apparatus. These delivery mechanisms  230  may be controlled by an entity  220  directing and/or benefiting from the delivery  228  to the processor-unit  120 , irrespective of where the server  226  may be located, or the computer readable memory  222  or disk drive  224  was written.
         As used herein, the Finite State Machine (FSM)  150  receives at least one input signal, maintains at least one state and generates at least one output signal based upon the value of at least one of the input signals and/or at least one of the states.   As used herein, the computer  152  includes at least one instruction processor and at least one data processor with each of the data processors instructed by at least one of the instruction processors. At least one of the instruction processors responds to the program steps of the program system  300  residing in the memory  154 .   As used herein, the Inferential Engine  158  includes at least one inferential rule and maintains at least one fact based upon at least one inference derived from at least one of the inference rules and factual stimulus and generates at least one output based upon the facts.   As used herein, the neural network  160  maintains at list of synapses, each with at least one synaptic state and a list of neural connections between the synapses. The neural network  160  may respond to stimulus of one or more of the synapses by transfers through the neural connections that in turn may alter the synaptic states of some of the synapses.       

       FIG. 10  shows some details of the program system  300  of  FIG. 9  that may include one or more of the following program steps:
         Program step  302  supports first-generating the vibration report  70  in response to the vibration readings  62 .   Program step  304  supports second-generating at least part of the vehicle parameters  200 - 208  of the vehicle  6  in response to the vibration readings  62  and/or the vibration report  70 .   Program step  306  supports third-generating the vehicle classification  220  of the vehicle  6  in response to one or more of the vehicle parameters  200 - 208 .   Program step  308  supports fourth-generating the weight estimate  210  and/or the deflection estimate  212  in response to the vibration readings  62  and/or the vibration report  70 .   Program step  310  supports fifth-generating the vehicle travel record  230  for the vehicle  6  in response to the vehicle classification  220 , the weight estimate  210 , the deflection estimate  212 , the vehicle identification  232  and/or the vehicle movement estimate  22 .   Program step  312  supports sixth-generating the at least one of the traffic ticket message  250 , the tariff message  252  and/or the insurance message  254 , each for the vehicle  6  in response to the vehicle travel record  230 .       

     Let ζ={t→z(t), t ∈ (t 0 , t 1 )} denote a succession of measurement samples of the vibration  34  as reported by the vibration sensor  60 . The vibration sensor  60  may report these vibrations  34  as a sequence of vibration readings  62  arranged in time t. 
       FIG. 11  shows some details of the program steps  302 ,  304 , and/or  308  of  FIG. 10  that may include one or more of the following program steps:
         Program step  320  supports upsample filtering at least two of the vibration readings  62  to generate at least one frequency-doubled vibration reading. As used herein, an upsample filter generates more samples output than sample inputs. In some contexts, the upsample filter may be decomposed into upsampling and a second filtering at least part of the upsampled data stream to emulate increasing the sampling frequency without having to operate the sensor more often.   Up-sampling may be implemented in a variety of ways. For example, each input sample may be replicated one or more times. Another example, each input sample may have a fixed value, such as zero inserted between it and the next input sample. Another example, the input sample may be inserted between a running and/or windowed average of the input stream.   The second filter may be composed of two or more subband filters whose outputs are sub-sampled so that the output rate of the second filter may be the same the up-sampled input stream rate, which may then be twice or more times the input stream rate of the upsampled filter.   Program step  322  supports noise-reducing the vibration readings  34  and/or the frequency-doubled reading to generate at least two quiet-vibration readings. In some implementations, noise-reducing processes the sensor measurement sample ζ to remove frequencies above min {6, 2.47 v} Hz and frequencies below 0.1 Hz. These or similar cutoffs may be arrived at empirically.   Program step  324  supports peak-estimating the vibration readings  34  and/or the frequency-doubled reading and/or the quiet-vibration readings to generate at least one peak estimate. This program step may take a moving average of measurements to estimate the magnitude and time at which the pavement  8 &#39;s vibration  34  achieves a negative and positive (local) peak, often referred to as a local extrema.       

     In some implementations, all measurements may filtered by the noise-reducing step before being processed by such program steps as up-filtering, peak-estimating and so on. 
       FIG. 12  shows an example of some details of the program steps  304  second generating the vehicle parameter  200  of  FIG. 10  that may include the following program step:
         Program step  330  supports axle-detecting to generate the axle count estimate  204  and the axle-spacing vector  206 . This program step may take the results of the peak-estimating program step  324 , partition the sample into different segments to isolate the response of individual vehicles  6 , and, if there is more than one embedded vibration sensors  49 , takes the maximum of the signals from different sensors to boost the signal-to-noise ratio. It may identify the occurrence of a negative or positive peak with an individual axle to generate the axle count estimate  204  in each vehicle  6 , and knowing the movement estimate  22  gives the spacing between axles as the axle spacing vector  206 .       

       FIG. 13  shows an example of some details of the program step  306  third generating the vehicle classification  220  of  FIG. 10  that may include the following program step: Program step  332  supports classifying the vehicle  6  based upon the axle count estimate  204  and the axle-spacing vector  206  to generate the vehicle classification  220 . 
     This program step  332  may classify vehicles  6  in accord with the FHWA classification scheme in the United States. 
     Other examples of the details of the program step  306  may classify vehicles  6  in accord with a different nation&#39;s, state&#39;s and/or province&#39;s standard classification scheme. 
       FIG. 14  shows some details of the program steps  308  fourth generating the weight estimate  210  and/or the deflection estimate  212  of  FIG. 10  that may include the following program steps:
         Program step  340  supports modeling a deflection  31  of the pavement  8  by the vehicle  6  to create the deflection estimate  212 .   Program step  342  supports determining the weight estimate  210  based upon the deflection  31  of the pavement  8 , for instance, based upon the deflection estimate  212 .   Program step  344  supports recognizing the dynamic weight component  216  in the weight estimate  210  to reveal the static weight estimate  214 . Note that in some embodiments, an averaging of the weight estimates  210  from multiple columns of the embedded wireless vibration sensor nodes  49  as shown in  FIG. 8B  may further generate the static weight estimate  214 . Also note, that determining the dynamic weight component  216  may be performed and the weight estimate  210  combined with the dynamic weight component  216  may be used by the system  10  to reveal the static weight estimate  214 .       

     Consider the following model of the deflection  31  of the pavement  8 : Assume the pavement  8  is an Euler beam. The deflection  31  is denoted by y(x, t) at position x and time t in response to a load on a single axle, say one of  21 ,  22  or  23  of  FIG. 1 . The deflection  31  may approximated as
 
 y ( x,t )= Fγ   −1   Re[Ψ *(ν t−x ) e   iω     0     t ]  (1)
 
     Here F may denote the axle load, ω 0  may denote the fundamental frequency of the axle suspension system, v may denote the vehicle speed, γ may denote a constant, and the pavement response ψ* may have a functional form as a complex function of position and time; both γ and ψ* depend upon parameters of the pavement  8  such as stiffness. The signal  34  measured by the vibration sensor  60  placed at x may be approximated as 
     
       
         
           
             
               
                 
                   
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     Consider some of the signal processing aspects of the system  10  and its processors  100 - 110  in which η is a constant, w is measurement noise originating in the electronic circuitry of the wireless vibration sensor node  49  and random pavement  8  vibrations  34 . Differentiating (1) twice shows that in this model acceleration is linear in axle load F and v 2 . The displacement of a real pavement  8  may not follow the ideal model, however the acceleration (and displacement) may often increase monotonically with the load F and speed v. Also, the greater the vehicle speed v, the higher will be the frequencies in the signal. 
     The disclosed method may include steps initializing at least one of the apparatus  10 ,  100 - 110 ,  49  and/or  90 , and/or operating at least one of the apparatus and/or using at least one of the apparatus to create at least one of the vibration report  70 , the vehicle parameter  200 - 208 , the weight estimate  210 , the deflection estimate  212 , the vehicle classification  220 , the vehicle travel record  230 , the traffic ticket message  250 , the tariff message  252 , and/or the insurance message  254 , each for the vehicle  6 . The vibration report  70 , the vehicle parameter  200 - 208 , the weight estimate  210 , the deflection estimate  212 , the vehicle classification  220 , the vehicle travel record  230 , the traffic ticket message  250 , the tariff message  252 , and/or the insurance message  254  are produced by various steps of the method. 
     Modeling the deflection  31  of the pavement  8  may integrate twice the noise-reduced response for each axle  21 ,  22 , and/or  23  to create the deflection estimate  212 . The peak deflection and speed can be used in a lookup table to estimate axle load, which may represent the weight estimate  210 . The table may be built using calibrated vehicles  6 . 
     The inventors have performed field tests using a system  10  similar to the system  10  shown in  FIG. 8 . Test results from three different sites indicate that the measurements are repeatable, and the system  10  correctly detects axles, and estimates pavement deflection  31  accurately and axle load well. The system  10  directly measures deflection  31  of the pavement  8  as the vehicle  6  goes over it, unlike current WIM stations that measure deflection of a plate, isolated from the pavement. The system  10  can be installed in minutes and takes up no space in or next to the lane  2 . It may be used in settings where current WIM stations are inappropriate, including weighing vehicles  6  on urban streets, and a vehicle weight-based tolling system. 
     The preceding discussion serves to provide examples of the embodiments and is not meant to constrain the scope of the following claims.