HK1260153A1 - Road monitoring method and system - Google Patents

Description

Road monitoring method and system

Technical Field

The invention relates to a method and a system for monitoring road conditions including road surface flatness.

Background

The need to measure Road flatness has led to a variety of different measuring devices ranging from responsive-Type Road flatness measuring Systems (RTRRMS) to more complex and specialized profilometers and the like.

Road flatness is characterized by fluctuations along the longitudinal axis of the road. Road flatness can be expressed using different measurement standards or flatness indices, where the International flatness Index (IRI: International Roughness Index) is an International standard. IRI is a mathematical representation of: the cumulative suspension travel of the vehicle is divided by the distance traveled. Thus, IRI has units of slope. When calculating IRI, the longitudinal road profile (road profile) is measured using 1/4 vehicle simulation (quater-car simulation). IRI is a flatness index that is reproducible, portable, and constant over time. Another flatness index commonly used is the Half Car (Half-Car) flatness index (HRI), which relates to averaging the left and right hand curves of a vehicle before processing the data.

Currently, pavement evenness measurements are divided into four general categories in a broad sense, depending on the apparatus and method of operation, reproducibility of the measurement, and accuracy and precision of the measurement. The main categories of flatness measurements are:

class 1 uses a profilometer for measuring the road profile with the highest accuracy and precision. The maximum longitudinal sampling interval is less than or equal to 25 mm. The precision of vertical elevation (elevation) measurement is less than or equal to 0.1 mm;

class 2 uses a profiler that can accurately measure the profile of a road. The maximum longitudinal sampling interval is more than 25mm and less than or equal to 150 mm. The precision of the vertical elevation measurement is more than 0.1mm and less than or equal to 0.2 mm.

Category 3 uses responsive devices calibrated by correlating the obtained measurements with known IRI values for a particular road segment. The maximum longitudinal sampling interval is more than 150mm and less than or equal to 300 mm. The precision of the vertical elevation measurement is more than 0.2mm and less than or equal to 0.5 mm.

Category 4 uses devices that do not perform calibration and include subjective assessment of road flatness to make measurements. The measurements are not suitable for network level monitoring. The maximum longitudinal sampling interval is >300 mm. The precision of the vertical elevation measurement is >0.5 mm.

The flatness measurements of category 1 and category 2 were obtained from very expensive profilometers that provided detailed indications of the road conditions. In fact, it is not possible or practical to regularly use these profilers throughout an extended road network to provide an accurate measurement of the condition of all roads in the network.

As an alternative to using profilometers to measure the flatness of all roads within an extended road network, it has been proposed (in the form of RTRRMS) to use a device of the type class 3 to monitor roads, including unpaved roads, continuously in real time by means of a device permanently mounted on a vehicle using an extended road network.

The two most pressing challenges when using RTRRMS devices remain: calibration of these devices, and correlation of data received by using these devices to a common scale of interest. More specifically, factors that affect the results obtained by these systems and continue to present challenges in calibrating these devices include: the host vehicle's suspension system, the vehicle dimensions, the load on the vehicle, the type, size and inflation pressure of the tires used on the vehicle, and the vehicle's travel speed over the measured time.

Disclosure of Invention

It is an object of the present invention to propose a method and a system for providing an indication of the evenness of a portion of a roadway by which the applicant believes the aforementioned disadvantages may at least be alleviated or which may provide advantageous alternatives to known systems and methods.

According to a first aspect of the invention, a method of providing an approximation of a flatness value based on a flatness index for a part of a road is proposed, the method comprising:

-receiving speed data of a first vehicle travelling along a portion of a road and receiving measured acceleration data of a device perpendicular to the road surface from a measuring device carried on the first vehicle;

-processing the acceleration data to provide a parameter value related to the acceleration data for a portion of the road; and is

-converting the parameter into an approximation of a flatness value based on a flatness index for a part of the road using the speed data and a first speed-based conversion equation.

A first speed-based conversion equation may be selected from a first set of speed-based conversion equations based on the speed data. The first set may include a plurality of conversion equations, each conversion equation of the first set being associated with a different predetermined speed.

The first velocity-based equation may be optional based on the velocity data. In other embodiments, the first velocity-based equation may have velocity data as a variable.

Each speed-based conversion equation of the first set may be derived in advance by:

-obtaining an actual flatness value for each reference portion using a measured flatness profile based on flatness indices of reference road segments having varying flatness and comprising a plurality of adjacent reference portions;

-obtaining acceleration data perpendicular to the road segment from first reference measurement means mounted on a first reference vehicle having travelled along a reference road segment at respective ones of different predetermined speeds, and determining reference parameter values relating to the acceleration data for adjacent portions of the reference road segment; and is

-deriving a relation between the reference parameter values and the actual flatness values for all reference portions.

The first set of speed-based conversion equations may be pre-stored in the memory component and may be associated with a first type of vehicle. Multiple types of vehicles may be defined, while sets of speed-based conversion equations for the various types of vehicles defined may be pre-derived.

Various types of vehicles may include at least some of the following: small hatchback vehicles, medium hatchback vehicles, compact cars, medium cars, Sport Utility Vehicles (SUVs), minibuses, and minivans.

The measurement device may be securely mounted on the vehicle, may move in unison with the vehicle, and may be a vehicle telematics device that is hidden by the body of the vehicle. The vehicle telematics device may include: a three-axis accelerometer, a three-axis gyroscope, a Global Positioning System (GPS) that measures latitude, longitude and speed data of the telematics device, a local processor with associated memory components, and a Radio Frequency (RF) transceiver that enables wireless data communication between the device and a central back-end.

The acceleration data and the velocity data may be periodically transmitted via the transceiver to the central back end to be processed.

The flatness index may be one of an international flatness index (IRI) and a semi-skid index (HRI).

The parameter value may be a statistical parameter value obtained by statistically processing z-axis acceleration data, and may be a Coefficient of Variation (CoV) defined as a ratio between a standard deviation (σ) and a mean value (μ) of acceleration data received for a portion of a road.

Alternatively, the parameter values may be mathematical parameter values.

The acceleration data may be processed with a local controller of the telematics device to provide parameter values. The parameter values of the neighboring segments may be periodically transmitted to the central back end via the RF transceiver to convert the parameter values of the neighboring segments to an approximation of a flatness value based on the flatness index, and the parameter values of the neighboring segments are combined to generate an approximated flatness profile based on the flatness index for the road segment. The curves may be distributed to users in the form of a visual representation that may include a map representing the flatness of the road based on predetermined critical locations.

The acceleration data may be sampled by the measuring device at a rate of 80Hz to 800Hz, and the length of the portion of the road may be 1m to 100 m.

According to a second aspect of the invention, a system is proposed that can provide for a portion of a road an approximation of a flatness value based on a flatness index, the system comprising:

-a central back end;

-a fleet of vehicles, each vehicle comprising: measuring means for measuring acceleration data perpendicular to a portion of the road and for providing speed data of the vehicle along a portion of the road; and the measuring device has a Radio Frequency (RF) transmission device for communicating with the central backend;

-a processor for processing acceleration data measured by the measuring device into parameter values; and

-a memory component comprising a first speed-based conversion equation for converting a parameter value into an approximation of a flatness value based on a flatness index;

the measurement device may be a vehicle telematics device that includes a controller and a memory component. Alternatively, the back-end may include a memory component.

According to a third aspect of the invention, a computer-readable medium is proposed, having a computer program with a program code for performing the method of claim 1, when the program is run on a processor.

According to a fourth aspect of the present invention, a computer-readable medium is proposed, having stored thereon data relating to at least a first pre-derived speed-based conversion equation for use in a computer program to be run on a processor for performing the method of claim 1.

Drawings

The invention will now be further described, by way of example only, with reference to the accompanying drawings, in which:

FIG. 1 is a schematic diagram illustrating a system for monitoring road conditions;

FIG. 2(a) is a block diagram illustrating a method for providing an approximation of a flatness value based on a flatness index, such as HRI or IRI, for a portion of a road;

FIG. 2(b) is a block diagram illustrating a method of deriving multiple sets of speed-based conversion equations;

FIG. 3 is a schematic diagram representing a reference segment comprising a plurality of reference parts for use in the derivation according to the method shown in FIG. 2 (b);

FIG. 4 is an actual flatness curve for the reference road segment of FIG. 3 obtained by a class 1 profiler;

FIG. 5 illustrates CoV values obtained by driving a reference vehicle at three different predetermined speeds over the reference road segment of FIG. 3; and is

FIG. 6 is a first set of transformation equations derived from regression analysis of the data of FIGS. 4 and 5.

Detailed Description

An exemplary embodiment of a system for monitoring road conditions is designated generally by the reference numeral 10 in fig. 1.

The road 12 to be monitored may be any road within a larger road network and is divided into adjacent sections (14.1 to 14.m), each section being of equal length, such as 100m or 10m, etc. The system 10 is arranged to provide an approximation of a flatness value based on a flatness index for each of the sections 14.1 to 14.m of the road 12. A first vehicle 16 associated with the x, y, and z axes travels along the roadway 12. The z-axis is perpendicular to the road surface. As the first vehicle 16 travels along the roadway 12, a first measurement device 18 carried by the first vehicle 16 measures and records data in the form of z-axis acceleration of the first device 18. Data relating to the speed at which the first vehicle 16 is traveling along the roadway 12 is also obtained.

The system 10 is arranged to perform a method of providing an approximation of a flatness value based on a flatness index for each of the sections 14.1 to 14.m of the road 12. The method is designated as a whole by the reference numeral 20 of fig. 2 (a).

The measurement device 18 measures z-axis acceleration data of the device 18 as the vehicle 16 travels along the roadway 12 and over the portions 14.1 to 14. m. As described above, speed data of the vehicle 16 is also obtained. The z-axis acceleration measurements are sampled at a predetermined frequency such that a plurality of measurements are made in each of the sections 14.1 to 14. m. As the vehicle travels over respective ones of the portions 14.1 to 14.m of the roadway 12, the z-axis acceleration data for the respective portions is processed to provide parameter values related to the z-axis acceleration data measured by the first measuring device 18.

The parameter values are converted to an approximation of the flatness values based on the flatness index using a first velocity-based conversion equation using the velocity data.

In an exemplary embodiment, the first measurement device 18 is a vehicle telematics device of a known kind that is used for pursuit of stolen/hijacked vehicles, insurance purposes including monitoring driver behavior, and fleet monitoring and management. The telematics device 18 is securely mounted to the first vehicle 16 and hidden by the body of the vehicle 16. The telematics device 18 thus moves in unison with the vehicle, which enables it to measure the z-axis vibration and acceleration experienced by the vehicle 16 due to the flatness of the road. In other embodiments, the device may be removably but rigidly connected to the body or chassis in a stand or the like. The vehicle telematics device 18 includes: a three-axis accelerometer 22, a three-axis gyroscope 24, a Global Positioning System (GPS)26, a local controller 28 having a processor and associated memory component 30, a Radio Frequency (RF) transceiver 32 that enables wireless data communication between the telematics device 18 and a central back end 34, and a local power supply for the device that includes a battery 36.

The position of the sections (14.1 to 14.m) of the road 12 is determined by means of GPS which provides longitude and latitude data with an accuracy of about 2.5 round Error (CEP).

The transceiver 32 periodically transmits data to the back end 34. The frequency (sampling rate) at which the z-axis acceleration data is measured, the capacity of the memory component 30, and the processor 28 of the telematics device 18 can affect the rate at which the data is transmitted to the back end 34.

In the exemplary embodiment, back end 34 includes a computer system 37 having a processor component 38 and a memory component 39; and a receiver 40 that enables wireless communication between the telematics device 18 and the backend 34. Wireless Communication between telematics device 18 and backend 34 may be via a Global System for Mobile communications (GSM) network 42. The GPS 26 of the telematics device communicates with the satellite 44 in a known manner.

The flatness index that is the basis of the approximate acceleration data may be one of an international flatness index (IRI) and a semi-vehicle flatness index (HRI).

In an exemplary embodiment, and referring also to fig. 2(a), the parameter values are statistical parameter values, and may be obtained by statistically processing acceleration data for each of the sections 14.1 to 14.m of the roadway 12 to obtain a coefficient of variation (CoV) for a particular section related to z-axis acceleration of the device 18. In determining the CoV, a standard deviation (σ) (shown as 200 in fig. 2 (a)) and a mean (μ) (shown as 202 in fig. 2 (a)) of acceleration data (shown as step 204 in fig. 2 (a)) received for a portion of the road 12 are calculated. Then, CoV is obtained in 206 of fig. 2(a) according to the following equation:

CoV is a dimensionless quantity of dispersion. It is commonly used to measure variability or discreteness of data related to the mean of the distribution. It is more simply defined as the ratio of the standard deviation to the mean of the data. The dimensionless nature of CoV allows easier comparison of data from different vehicles or vehicles traveling at different speeds.

In the case where the available memory section 30 is insufficient, the memory can be changed by using a naive coefficient of variation (NCoV:coefficients of Variation) to approximate the value of CoV, which is defined as:

when NCoV is used, therefore, the standard deviation used in the CoV calculation is replaced by the running standard deviation (or the na iotave deviation) of the z-acceleration data.

Throughout the specification, references to CoV should be understood to include the use of NCoV as an alternative.

As will be explained in more detail below, the correlation between the trend of the different CoV data plots and the actually measured HRI-curve is significant when comparing fig. 4 and 5. Regions of high CoV value can be associated with regions of high road flatness.

Nevertheless, CoV is not suitable in itself to provide a practical approximation of flatness values and requires further processing. This is evident from the vertical dispersion obtained when plotting the CoV curves of different vehicles travelling at different speeds on a road. The data output shown in FIG. 5 can be thought of as a standard first order data set that can be obtained from a data cloud collected from all of a large number of vehicles equipped with telematics device 18.

It is worth noting that when calculating the CoV value, the standard deviation and mean of the z-axis acceleration should always be positive and should not fluctuate around the zero axis. Thus, the gravity component of the z-axis acceleration is preserved in the calculation.

Preferably, the sampling rate of the data is 100Hz and the length of each of the portions 14.1 to 14.m of the link 12 is 100 m. At this frequency and at an exemplary speed of 100km/h, z-axis acceleration data is sampled every 278mm in each of the sections 14.1 to 14.m of the road 12, which corresponds well to class 3 flatness measurements. As the data storage, processing and transmission capabilities of the system 10 increase, the length of the portion can be reduced to 10m to increase the accuracy of the results obtained thereby. Other sampling rates, such as 80Hz, are possible. However, the higher the sampling rate, the more accurate the approximation will be. The new generation of telematics devices are capable of up to 400Hz sampling rates.

Thus, for each of the portions 14.1 to 14.m of the road 12, a single CoV value based on all the acceleration data points sampled is determined.

Instead of determining CoV values as described above, Root Mean Square (RMS) values may instead be determined for each section 14.1 to 14. m. However, it has been found that using CoV instead of RMS, particularly at a sampling frequency of about 100Hz, results in a stronger correlation with the actually measured flatness based on the flatness index (IRI or HRI).

The advantages of the current system are related to this: instead of using actually measured displacement data (as used in known profilometers), readily available z-axis acceleration data is used, without the need to convert the acceleration data into displacement data. However, if necessary, the acceleration data can be converted into displacement data by means of double integration. Therefore, a mathematical process of converting acceleration data into displacement data may be used instead of the statistical process as described above. This necessarily has a negative impact on processing requirements.

The relatively low rate of change in the travel speed of the vehicle 16 compared to the rate of change in the z-axis acceleration means that the speed data sampling rate of the vehicle 16 may be different than the sampling rate of the z-axis acceleration. Typically, the velocity data is measured at a frequency of 1Hz (as shown at 208 in FIG. 2 (a)), and linear interpolation is used to assign velocity data values to each z-axis acceleration value. The velocity data is processed such that a single velocity data value is assigned to each of the portions 14.1 to 14.m and hence to each CoV value.

The velocity data 208 is used to convert the CoV value 206 into an approximation of a flatness value based on a flatness index (IRI or HRI) for each of the sections 14.1 to 14. m. The conversion of the CoV value 206 to an approximation of the flatness value is illustrated at 210 in fig. 2 (a). This is done by using a speed-based conversion equation that depends on the measured vehicle speed. A suitable speed-based transformation equation (shown as 212 in figure 2 (a)) is selected from a first set of pre-derived speed-based transformation equations 80.1 (shown in figure 6). Each conversion equation of the first set 80.1 relates to one of a plurality of different predetermined speeds. In the exemplary embodiment, a first set of conversion equations 46.1 is stored on memory component 39 of back end 34.

The method of deriving the speed-based conversion equation is indicated generally by the reference numeral 250 of fig. 2 (b). To derive the conversion equation, the reference segment 60 shown in FIG. 3 (shown at 252 in FIG. 2 (b)) is selected and divided into a plurality of reference portions 62. The reference section 60 has a known length l and a flatness that varies along its length l. The length l of the reference road segment 60 must be sufficient to provide a wide range of varying road flatness. The actual flatness profile (indicated at 254 in fig. 2 (b)) of the reference section 60 (preferably based on class 1) is measured with a known profilometer for a known flatness index (IRI or HRI). Fig. 4 illustrates an example of an actual flatness curve 64 in terms of HRI flatness index determined by a profilometer for a reference road segment 60. From fig. 4, the actual flatness values in terms of HRI for the portions 62 of the reference road 60 may thus be determined (shown as 256 in fig. 2 (b)).

On the reference section 60 there is driven a first reference vehicle (not shown) equipped with a first reference measuring device comprising at least an accelerometer. To derive the speed-based conversion equation for each predetermined speed, a first reference vehicle travels at each predetermined speed on the reference road segment 60.

The z-axis acceleration data obtained from the reference measurement device is processed to obtain CoV values for the various portions 62 of the reference road segment 60 (calculations of CoV for the various portions 62 are made as described above, the calculations being shown in 258 of fig. 2 (b)).

Fig. 5 illustrates three different curves in terms of reference statistical parameters (in the case of CoV) obtained from a reference measurement device. By way of example, the curves 52, 54 and 56 refer to examples in which the first reference vehicle travels along the reference stretch 60 at speeds of 40km/h, 50km/h and 60km/h, respectively. It will be appreciated that in practice the conversion equation will be derived using many different predetermined speeds.

From a comparison of the actual curve 64 of fig. 4 with the curves (52, 54 and 56) of fig. 5, it is clear that all curves have corresponding shapes. This shows a strong correlation between the reference CoV value and the actual flatness based on the flatness index. However, the difference in the values of the curves (52, 54, and 56) of fig. 5 clearly indicates the need to correlate the data with a common scale of interest.

For each speed, the relationship is derived by comparing the reference CoV value with the actual flatness value for each of the reference sections 62 of the reference roads 60 (shown at 260 in fig. 2 (b)). The derivation of the foregoing relationship is illustrated at 262 in fig. 2(b), and the resulting conversion equation is illustrated in fig. 6. The conversion equations 82, 84, and 86 as shown in fig. 6 include regression analysis of the reference statistical parameter values and the actual flatness values for each of the reference portions 62 of the reference roads 60. Conversion equations 82, 84, and 86 correspond to CoV curves 52, 54, and 56, respectively. Thus, the conversion equations (82, 84, and 86) all correspond to a particular speed.

Thus, FIG. 6 represents an exemplary embodiment of the first set of conversion equations 46.1. Depending on the vehicle speed and as indicated at 212 of fig. 2(a), a suitable equation is selected from the first set of equations 80.1 and is used to convert parameters relating to acceleration data obtained by any vehicle for any road portion 14.1 to 14.m of the road 12 in the road network into an approximation of the flatness value for the HRI, as indicated at 210 of fig. 2 (a).

By way of example only, and referring again to FIG. 1, a vehicle 16 traveling at 50km/h on a link 12 (which need not necessarily be reference to link 60) records acceleration data and speed data on a particular portion 14.2 of the link 12. Statistical processing of the acceleration data for section 14.2 results in a CoV value equal to x 1. When this value is combined with the conversion equation 84 (which is selected based on a speed of 50 km/h), the HRI approximation value y1 is obtained. This process is repeated for the acceleration data and the speed data of the various portions 14.1 to 14.m of the road 12, so that a flatness profile (generally class 3) for the road section 12 can be obtained, which approximates the profile measured by the profiler in terms of HRI.

No further calibration or normalization of the approximation data is required as it is already a statistical representation of the road profile. Therefore, the CoV of the acceleration (z-direction) data obtained by the conversion can be directly compared with the HRI for the section of the link.

To account for variations due to suspension type, size, payload, etc. of different classes of vehicles, multiple reference vehicles are used to derive multiple sets of speed-based conversion equations. The reference vehicles are classified into a number (k) of categories. At least one reference vehicle per category is utilized to derive a set of speed-based equations associated with that category. A set of conversion equations is derived for each of the k categories of the vehicle such that there will be k sets of conversion equations, of which set 46.1 is an example.

Categories include, but are not limited to, small hatchbacks, medium hatchbacks, sedans, medium sedans, small Sport Utility Vehicles (SUVs), large SUVs, minivans, large vans, and the like. Categories may also be defined for commercial vehicles, and may be based specifically on the payload of the vehicle. Classification of the vehicle will result in a more accurate approximation of the flatness value. Categories may thus be provided for vehicles of different makes and models.

Thus, when data is received from a vehicle 16 traveling on the road 12, the class of vehicle (shown as 214 in FIG. 2 (a)) will determine which set of conversion equations to utilize, and the speed of the vehicle will determine which particular conversion equation within the set to utilize. Then, as described above, the CoV value is converted to a flatness index value using the selected equation (shown in fig. 2(a) at 210).

If the velocity does not directly correspond to one of the equations in the set, then interpolation or extrapolation methods may be employed or used to approximate the value from the actual velocity.

By using the above method, approximate flatness values for each of the sections 14.1 to 14.m of the road 12 are thus obtained. By combining the approximate flatness values of all the sections 14.1 to 14.m that are adjacent, an approximate flatness profile (shown as 216 in fig. 2 (a)) of the road 12 is generated. As more vehicles of different classes travel along a particular road 12, the approximate flatness values for each section may be averaged to obtain a more accurate flatness profile for the road segment 12. Furthermore, by utilizing vehicle telematics devices already installed in fleet vehicles, a large portion of roads within a road network can be measured in a cost-effective manner.

The flatness profile can be used to inform the road maintenance service provider of the area that needs maintenance. The flatness profile can also be used to analyze the road surface degradation of roads in a road network in order to implement a preventive maintenance program. The curves may also be presented visually (shown at 218 of fig. 2 (a)) and distributed to users. This may be particularly useful for people who travel along unfamiliar roads or who travel at night. Road transport service providers can use the flatness profile to select paths that will cause as little damage to their vehicles (particularly their tires) as possible and will minimize maintenance requirements. Information can be displayed on a Portable Navigation Device (PND) and in a unique application on a smartphone.

The visual representation of the flatness profile may be in the form of a map that illustrates the degree of flatness of the road surface based on key locations, typically color-based key locations. Alternatively, the flatness profile may be communicated to the client in the form of a flatness report.

It should be understood that the step of processing the z-axis accelerometer data into statistical or mathematical parameter values may be performed by the controller 28 of the telematics device 18, in which case the CoV, RMS, or displacement data, along with the velocity data, would be periodically or intermittently transmitted to the back end 34; or this step may be performed in the backend 34 itself, in which case the raw z-axis acceleration data and velocity data would be transmitted periodically or intermittently from the telematics device 18 to the backend 34 as described above. The associated set of conversion equations may also be loaded onto the memory component 30 of the telematics device 18 so that the steps of processing the data into a CoV value at 206, selecting the appropriate conversion equation at 212, and converting the CoV value into an approximate flatness value based on the flatness index at 210 may be performed in the telematics device 18. In this example, only approximate flatness values for each of the sections 14.1 through 14.m will be periodically or intermittently transmitted to the back end 34.

Thus, the system 10 provides a cost effective way to monitor the condition of a vast road network. Even though the data obtained from the system 10 may be classified by category into category 3 flatness measurements, applicants have found that the correlation between the approximate flatness curve generated by the system and the actually measured flatness curve based on IRI or HRI index is strong enough to draw inferences. The system 10 may be used to provide a first level of analysis to prioritize the use of actual profilometers. This may help ensure that agencies may be able to obtain an indication of their road network conditions without directly obtaining funds for the type 1 road condition data. The system 10 and method 20 described and/or defined herein may also contribute to the safety and comfort of road users in that the determined estimated road flatness data may be plotted in a mapping application that can be distributed to road users. The above-mentioned disadvantages of current RTRRMS can be overcome or at least alleviated by utilizing the CoV value of the z-axis acceleration data and converting the CoV value according to the speed and class of the vehicle to obtain an approximation of the flatness of the road.

For calibration purposes, the reference segment 60 must include varying flatness and must be long enough to produce accurate correlation. Furthermore, the various classes of vehicles used to derive the different sets of transformation equations must represent fleet vehicles that typically use a road network. To further improve the accuracy of the conversion equation, more than one reference road may be used (e.g., different reference roads may be used when converting data relating to paved and unpaved roads).

Fig. 1 illustrates a diagrammatic representation of machine in the exemplary form of a computer system 37 and a vehicle telematics device 18 within which program code or a set of instructions, for causing the machine to perform any one or more of the methodologies discussed herein, may be executed. The machine operates, and is capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken by the machine. Further, while only a single machine is illustrated in various instances, the term "machine" shall also be taken to include any collection of machines that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methodologies discussed herein.

Exemplary machines 37 and 18 include respective processors (e.g., a Central Processing Unit (CPU) and an associated computer or machine-readable medium in the form of memory components 39 and 30, respectively, the memory components 39 and 30 having stored thereon software and data structures, equations or algorithms 46.1 through 46.k in the form of one or more sets of instructions 41, these sets of instructions 41 and data structures, equations or algorithms 46.1 through 46.k being embodied as or utilized by any one or more of the methodologies or functions described herein.

While the machine-readable media 39 and 30 are illustrated in an exemplary embodiment as a single medium, the term "machine-readable medium" should be taken to include a single medium or multiple media (e.g., a centralized or distributed database, and/or associated caches and servers) that store the one or more sets of instructions. The term "machine-readable medium" shall also be taken to include any medium that: a medium capable of storing, encoding or carrying a set of instructions for execution by a machine and that cause the machine to perform any one or more of the methodologies of the present invention; or capable of storing, encoding, or carrying data structures, equations, or algorithms utilized by or associated with the set of instructions. The term "machine-readable medium" shall accordingly be taken to include, but not be limited to, solid-state memories, optical and magnetic media, and carrier wave signals.

It will be appreciated that many variations of the present disclosure provided herein to illustrate or exemplify the invention are possible without departing from the overall spirit of the claimed invention. This variant is to be understood as forming part of the present invention. For example, the sampling rate, reporting frequency, speed at which the conversion equation is derived, category of vehicle, length of any road segment or section, etc. are not limited to the examples provided herein. The length of the portion may also vary according to customer needs, while the sampling rate may increase as telematics technology improves.

Claims (27)

1. A method of providing an approximation of a flatness value based on a flatness index for a portion of a road, the method comprising:

-receiving speed data of a first vehicle travelling along a portion of said road and receiving measured acceleration data of said device perpendicular to the road surface from a measuring device carried on said first vehicle;

-processing the acceleration data to provide a parameter value related to the acceleration data for a portion of the road;

-converting the parameter into an approximation of a flatness value based on the flatness index for a part of the road using the speed data and a first speed-based conversion equation.

2. The method of claim 1, wherein the first speed-based conversion equation is selected from a first set of speed-based conversion equations based on the speed data, the first set of speed-based conversion equations comprising a plurality of conversion equations, wherein each conversion equation of the first set is associated with a different predetermined speed;

3. the method of claim 2, wherein each speed-based conversion equation of the first set is pre-derived by:

obtaining an actual flatness value for each reference portion using a measured flatness curve based on flatness indexes of a reference road section having a varying flatness and including a plurality of adjacent reference portions;

obtaining acceleration data perpendicular to the road segment from a first reference measurement device mounted on a first reference vehicle that has travelled along the reference road segment at respective ones of the different predetermined speeds, and determining reference parameter values relating to the acceleration data for adjacent portions of the reference road segment; and is

Deriving a relationship between the reference parameter value and an actual flatness value for all reference portions.

4. The method of any one of claims 2 and 3, wherein the first set of speed-based conversion equations is pre-stored in a memory component.

5. The method of any of claims 2-4, wherein the first set of speed-based conversion equations is associated with a first type of vehicle.

6. The method of claim 5, wherein a plurality of types of vehicles are defined, and wherein each set of speed-based conversion equations for each of the defined types of vehicles is derived in advance.

7. The method of claim 6, wherein the plurality of types of vehicles include at least some of: small hatchback vehicles, medium hatchback vehicles, compact cars, medium cars, Sport Utility Vehicles (SUVs), minibuses, and minivans.

8. A method according to any preceding claim, wherein the measuring device is fixedly mounted on the vehicle such that it moves in unison with the vehicle.

9. The method of claim 8, wherein the measurement device is a vehicle telematics device concealed by a body of the vehicle, the vehicle telematics device comprising: a three-axis accelerometer, a three-axis gyroscope, a Global Positioning System (GPS) that measures latitude, longitude and speed data of the telematics device, a local processor with associated memory components, and a Radio Frequency (RF) transceiver that enables wireless data communication between the device and a central back-end.

10. The method of claim 9, wherein the acceleration data and the velocity data are periodically transmitted via the transceiver to the central backend to be processed.

11. The method of any one of the preceding claims, wherein the flatness index is one of an international flatness index (IRI) and a semi-vehicular index (HRI).

12. The method according to any of the preceding claims, wherein the parameter value is a statistical parameter value obtained by statistically processing z-axis acceleration data.

13. The method of claim 13, wherein the statistical parameter is a coefficient of variation (CoV) defined as a ratio between a standard deviation (σ) and a mean (μ) of the acceleration data received for a portion of the road.

14. The method of any one of claims 1 to 12, wherein the parameter value is a mathematical parameter value obtained by mathematically processing the acceleration data.

15. The method of claim 9, wherein the acceleration data is processed with a local controller of the telematics device to provide the parameter values, and wherein the parameter values of adjacent portions are periodically transmitted to the central backend via the RF transceiver to convert the parameter values of the adjacent portions to an approximation of a flatness value based on the flatness index.

16. The method according to any of the preceding claims, wherein the approximate flatness values of adjacent parts of the road section are combined to generate an approximate flatness profile of the road section based on the flatness index.

17. The method of claim 16, wherein the approximated flatness profile is distributed to users in a visual representation.

18. The method of claim 17, wherein the visual representation comprises a map representing the flatness of the road based on predetermined critical locations.

19. The method according to any one of the preceding claims, wherein the acceleration data is sampled by the measuring device at a rate of 80Hz to 800 Hz.

20. The method according to any of the preceding claims, wherein the length of the portion of the road is 1 to 100 m.

21. A system for providing an approximation of a flatness value based on a flatness index for a portion of a road, the system comprising:

a central back end;

a fleet of vehicles, each of the vehicles comprising: a measuring device for measuring acceleration data perpendicular to a portion of the roadway and for providing speed data of the vehicle along a portion of the roadway; and the measuring device having a Radio Frequency (RF) transmission device for communicating with the central backend;

a processor for processing the acceleration data measured by the measurement device into parameter values;

a memory component including a first speed-based conversion equation to convert the parameter value to an approximation of a flatness value based on a flatness index;

22. the system of claim 21, wherein the first speed-based conversion equation forms part of a first set of speed-based conversion equations, each conversion equation of the first set being associated with a different predetermined speed.

23. The system of claim 22, wherein the fleet of vehicles is divided into a plurality of categories, and wherein a different set of speed-based equations is provided for each category.

24. The system of any one of claims 21 to 23, wherein the measurement device is a vehicle telematics device.

25. The system of any one of claims 21 to 23, wherein the backend comprises the memory component.

26. A computer-readable medium having a computer program with a program code for performing the method of claim 1, when the program runs on a processor.

27. A computer readable medium having stored thereon data relating to at least a first pre-derived speed-based conversion equation for use by a computer program running on a processor to perform the method of claim 1.