Method for Vehicle Classification Using Multiple Geomagnetic Sensors

Abstract

The invention discloses a method for vehicle classification using multiple geomagnetic sensors, which mainly solves the problems of high cost, complex processing process and difficulty in large-scale deployment of the existing vehicle classification methods. The method comprises the following steps: sequentially deploying N geomagnetic sensors on a road side at equal intervals d, each of the geomagnetic sensors respectively collecting magnetic field data around same, respectively transmitting the magnetic field data to a data processing module for storage, and judging whether a vehicle passes over a detection range of the sensors or not according to the data; calculating a time difference obtained when the vehicle passes by two adjacent sensors among N sensors, and calculating the vehicle speed and the vehicle magnetic length according to the time difference; setting a vehicle magnetic length double-threshold value and a Z axis magnetic field strength threshold value, acquiring Z axis geomagnetic data and the magnetic length that the vehicle passes by, comparing the Z axis geomagnetic data and the magnetic length that the vehicle passes by with the set threshold value, and acquiring a judged vehicle type result. According to the invention, the vehicle type information of the vehicle passing by can be accurately acquired, the reliability is high, the cost is low, large-scale deployment is easy to realize, and the method can be used for highway intellectualization.

Claims

1. A method for vehicle classification using multiple geomagnetic sensors, wherein, the method comprises the following steps: 1) sequentially deploying N geomagnetic sensors on a road side at equal intervals d, and a vehicle sequentially passing by each of the sensors when it runs, wherein 2≤N≤10, 5 m≤d≤15 m; 2) N geomagnetic sensors respectively collecting magnetic field data around the sensors in real time and sequentially transmitting the magnetic field data to a data processing module, wherein the data processing module adopts a low-power-consumption microprocessor; 3) the data processing module analyzing the data transmitted by the N sensors: 3a) the data processing module judging whether or not a data mark sent by a first geomagnetic sensor indicates a vehicle: if so, judging that there is a vehicle passing by, and executing 3b), otherwise, returning to 2); 3b) the data processing module judging whether a data mark sent by a second sensor to a N.sup.th geomagnetic sensor indicates there is a vehicle or not: if so, judging that there is a vehicle passing by, and executing 3c), otherwise, returning to 2); 3c) the data processing module storing data about the vehicle passing by sent by the first geomagnetic sensor to the N.sup.th geomagnetic sensor, and adding a time stamp; 4) the data processing module aligning the stored data: 4a) finding out data at a time when the vehicle drives in the N geomagnetic sensors, and then finding out data at a time when the vehicle leaves the N geomagnetic sensors; 4b) respectively aligning data, i.e. first data, at an initial time when a vehicle drives in N geomagnetic sensors, and sequentially aligning second data, third data . . . and M data acquired by the N.sup.th sensor when the vehicle drives in, until aligning the data acquired when the vehicle leaves the N.sup.th sensor, wherein M is the number of data acquired by the sensor; 5) calculating a time difference Δt.sub.1,2, Δt.sub.2,3, . . . , Δt.sub.N-1,N obtained when the vehicle passes by two adjacent sensors among N sensors: 5a) sequentially calculating a time difference between the first data and a time difference between the second data between the first and second sensors after alignment, and a time difference between the M data until a time difference between the last data is calculated; 5b) taking an average value of the time differences among all the data, i.e. a time difference Δt.sub.1,2 obtained when the vehicle passes between the first sensor and the second sensor; 5c) sequentially calculating a time difference between the first data, a time difference between the second data . . . , and a time difference between the M data between the second and third sensors after alignment, until a time difference between the last data is calculated; 5d) taking a mean value of a time difference among all the data, i.e. a time difference Δt.sub.2,3 obtained when the vehicle passes between the second sensor and the third sensor; 5e) repeating steps 5a-5d to sequentially obtain a time difference Δt.sub.1,2, Δt.sub.2,3, Δt.sub.N-1,N between two adjacent sensors; 6) calculating an average time $\Delta t=\frac{\Delta t_{1,2}+\Delta t_{2,3}+\mathrm{.Math.}+\Delta t_{N-1,N}}{N-1}$ of a vehicle passing by the two adjacent sensors according to the time difference Δt.sub.1,2, Δt.sub.2,3, . . . , Δt.sub.N-1,N and calculating a running speed of the vehicle: $v=\frac{d}{\Delta t};$ 7) calculating a magnetic length of a vehicle when it passes by; 7a) setting an arrival threshold value and a departure threshold value of a vehicle, acquiring durations Δt.sub.1, Δt.sub.2, . . . , Δt.sub.N of the vehicle when it passes by N geomagnetic sensors respectively according to the recorded time stamps, and calculating an average duration of the vehicle when it passes by each of the sensors: $\Delta t^{\prime }=\frac{\Delta t_1+\Delta t_2+\mathrm{.Math.}+\Delta t_N}{N};$ 7b) calculating a magnetic length VML: VML=v×Δt′ of a vehicle when it passes by according to a running speed v of the vehicle and an average duration Δt′ of the vehicle when it passes by each of the sensors; 8) setting a Z axis magnetic field strength threshold value S, subtracting a geomagnetic base line from Z axis magnetic field data detected by N geomagnetic sensors respectively, and detecting whether data less than the threshold value S exist; if the N geomagnetic sensors exist, marking as ‘1’, otherwise, marking as ‘0’; 9) judging vehicle classification results: 9a) setting double-threshold values L1 and L2 of the vehicle magnetic lengths, and L1>L2; 9b) comparing the magnetic length of the vehicle when it passes by with L1 and L2; if the magnetic length of the vehicle is greater than or equal to L1 when it passes by, the vehicle is judged to be a large one; if the magnetic length of the vehicle is less than or equal to L2 when it passes by, the vehicle is judged to be a small one; searching and judging whether or not the mark corresponding to the current vehicle is ‘1’ if the magnetic length of the vehicle is smaller than L1 but greater than L2 when it passes by, the vehicle is judged to be a large one, otherwise, to be a small one.

2. The method according to claim 1, wherein: the geomagnetic sensor is selected from any one of a digital geomagnetic sensor, an analog geomagnetic sensor, a single-axis geomagnetic sensor and a multi-axis geomagnetic sensor.

3. The method according to claim 1, wherein: the magnetic field data in step 2) refers to fluctuation magnetic field data at a time when a vehicle passes by and relatively stable magnetic field data at a time when no vehicle passes by, which are detected by all the geomagnetic sensors, wherein the fluctuation range of the magnetic field when a vehicle passes by exceeds 50 nT, and the fluctuation range of the magnetic field when no vehicle passes by does not exceed 20 nT.

4. The method according to claim 1, wherein, the time stamp in 3c) refers to an instantaneous time at which the geomagnetic data is acquired by the sensor, and the instantaneous time is acquired by a clock module in the central processing or by time data uniformly transmitted by the base station.

5. The method according to claim 1, wherein, the data in 4b) are aligned at an initial time when the vehicle drives in a monitoring range of N geomagnetic sensors, at a time when the vehicle leaves N geomagnetic sensors, or at a time when the geomagnetic characteristics of N geomagnetic sensors are most obvious, i.e. at a time when the geomagnetic data fluctuate highest or at a time when the geomagnetic data fluctuate lowest.

6. The method according to claim 1, wherein, the vehicle magnetic length in 7) refers to a product of a time when a magnetic field disturbance is caused by the vehicle and a speed of the vehicle.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to illustrate the technical solutions of the embodiments of the invention more clearly, the drawings used in the description of the embodiments are briefly described below, and it is obvious that the drawings in the description below are some embodiments of the invention, and that other drawings can be obtained by a person skilled in the art without involving any inventive effort.

[0041] FIG. 1 is a flow chart of an implementation of the invention;

[0042] FIG. 2 is a schematic diagram showing deployment of multiple geomagnetic sensors according to the invention;

[0043] FIG. 3 is a schematic view illustrating data alignment of multiple geomagnetic sensors according to the invention;

[0044] FIG. 4 is a schematic diagram of geomagnetic waveforms provided by two adjacent geomagnetic sensors of the invention;

[0045] FIG. 5 is a schematic diagram of a Z axis magnetic waveform when different types of vehicles pass by according to the invention.

DETAILED DESCRIPTION OF THE INVENTION

[0046] Embodiments of the invention will now be described more fully hereinafter with reference to the accompanying drawings, in which it is apparent that the described embodiments are only a few, but not all embodiments of the invention. Based on the embodiments of the invention, all other embodiments obtained by a person skilled in the art without involving any inventive effort are within the scope of the invention.

[0047] Referring to FIG. 1, the method for vehicle classification using the multiple geomagnetic sensors of the present example is implemented as follows:

[0048] Step 1, multiple geomagnetic sensors are deployed according to actual requirements.

[0049] The multiple geomagnetic sensors are composed of N geomagnetic sensors and are deployed on the road side at equal intervals, connection modes between the N sensors and the data processing module are diversified, and all wireless communication modes can be included through wired connection or wireless connection. A distance between the sensors is set according to the actual situation of the road to be measured or the magnitude of the sensor system, the distance range is 5-15 m, and the time difference of the vehicle passing by the two sensors can be obtained through the deployment distance of the two adjacent geomagnetic sensors so as to obtain a speed of a vehicle. The geomagnetic sensor includes a digital geomagnetic sensor, an analog geomagnetic sensor, a single-axis geomagnetic sensor and a multi-axis geomagnetic sensor. The present example adopts RM 3100 digital three-axis geomagnetic sensors, but is not limited to other geomagnetic sensors in the market, large dynamic range linear sensors, which can indicate that the sensors in which the geomagnetic field varies are neither limited to single-axis, multi-axis geomagnetic sensors, nor geomagnetic sensors using digital and analog signals.

[0050] In practice, a geomagnetic sensor is deployed on a building or a road surface on one side of a road according to actual requirements, and the geomagnetic sensor can classify the types of vehicles no matter on one side of the road or the road surface.

[0051] Referring to FIG. 2, according to the present example, N geomagnetic sensors are deployed on one side of a road, and each geomagnetic sensor is sequentially arranged, i.e. a vehicle firstly passes by a first sensor 1, then passes by a second sensor 2, and finally passes by a N.sup.th sensor. The number N and the distance d of the geomagnetic sensors are correspondingly adjusted according to actual road requirements and technical requirements, meanwhile the embodiment is set as not to be limited to N=5, two adjacent sensors are set as not to be limited to d=10 m to mount according to the distance, and the communication mode between the geomagnetic sensors and the data processing module is set to be wired communication or wireless communication according to the requirements. This example uses wired communication. The power supply modes of the geomagnetic sensor and the data processing module can be all power supply modes of solar energy, wind energy, commercial power and the like, so that the geomagnetic sensor and the data processing module can realize uninterrupted work for 24 hours.

[0052] Step 2, multiple geomagnetic sensors collect surrounding geomagnetic field data.

[0053] As shown in FIG. 4, the waveform of the geomagnetic sensor varies with the vehicle passing by the road surface, i.e. when the vehicle passes by, data fluctuation of the geomagnetic field in the first sensor 1 is caused firstly, then the data fluctuation of the geomagnetic field of the second sensor 2 is caused, and finally the data fluctuation of the geomagnetic field of the fifth sensor 5 is caused. The five geomagnetic sensors acquire the data of the local magnetic field in real time, and the obtained data are transmitted to the data processing module through the communication mode in step 1 for processing.

[0054] The magnetic field data refer to fluctuation magnetic field data at a time when a vehicle passes by and relatively stable magnetic field data at a time when no vehicle passes by, which are detected by all the geomagnetic sensors, wherein the fluctuation range of the magnetic field when a vehicle passes by exceeds 50 nT and the fluctuation range of the magnetic field when no vehicle passes by does not exceed 20 nT.

[0055] The data processing module is mainly composed of a low-power-consumption processor and some peripheral circuits. According to the example, the low power processor is a processor of M3 series based on ARM architecture, but is not limited to other series of processors based on ARM authorization, which further includes a series of processors designed based on X86 and an ultra low power processor of MSP430 series.

[0056] Step 3, the data processing module analyzes the data transmitted by the five geomagnetic sensors and judges whether a vehicle passes by or not.

[0057] 3.1) According to the fluctuation condition of the magnetic field data in the first geomagnetic sensor 1, the data processing module judges that a vehicle passes by if 10 continuous data fluctuations of the magnetic field data of the first sensor 1 exceeds 60 nT, saves the geomagnetic data at a time when the vehicle passes by, and executes step 3.2), otherwise, returns to step 2; and

[0058] 3.2) the data processing module further judges whether or not the data marks sent by the second to fifth geomagnetic sensors indicate there is a vehicle, and the judgment mode is the same as that of step 3.1): if so, judging that a vehicle passes by, storing the part of geomagnetic data, otherwise, returning to step 2; and

[0059] Step 4, the data processing module adds a time stamp to the stored geomagnetic data.

[0060] 4.1) When the vehicle passes by, the data processing module finds an initial moment t.sub.0 when the vehicle reaches the detection range of the sensor, and acquires data one time at a time to acquire time information, wherein the time information refers to an instantaneous time of the moment when the sensor acquires geomagnetic data, and the method for acquiring the time can be acquired by a clock module of the processor and also can be acquired according to the time information in an instruction issued by a base station module.

[0061] The time information obtained in the example is acquired by the clock module in a processor, i.e. a sampling interval is acquired by a formula

[00004]$T=\frac{1}{f}$

according to a sampling frequency f of the magnetic field through the time t.sub.0 of the first geomagnetic data, and the time of each data is acquired through the time interval nT of the n geomagnetic data and the first geomagnetic data: t.sub.n=t.sub.0+nT;

[0062] 4.2) the time information acquired in step 4.1) is sequentially added into the corresponding magnetic field data until the vehicle leaves the last geomagnetic sensor 5.

[0063] Step 5, multiple geomagnetic data are aligned by the data processing module.

[0064] 5.1) the data processing module firstly finds data at a time when a vehicle respectively drives in the five geomagnetic sensors, then finds data at a time when the vehicle leaves the five geomagnetic sensors, takes data at a time when the vehicle initially drives in a first sensor 1 to a fifth sensor 5 as first aligned data, and takes data at a time when the vehicle leaves the first sensor 1 to the fifth sensor 5 as last aligned data;

[0065] 5.2) the first data, the second data, and the third data . . . of the first sensor 1 through the fifth sensor 5 are aligned, until the last data are aligned, as shown in FIG. 3.

[0066] Step 6, an average time difference Δt obtained when a vehicle passes by the two adjacent sensors is calculated.

[0067] 6.1) In the aligned data acquired in step 5, the time information of the first data t.sub.11, the second data t.sub.12 and the third data t.sub.13 . . . of the first sensor 1 and the time information of the first data t.sub.21 the second data t.sub.22 and the third data t.sub.23 . . . of the second sensor 2 are subtracted, and an average value is calculated to obtain a difference Δt.sub.1,2 between each of the data of the first sensor 1 and the second sensor 2;

[0068] 6.2) similarly, a time difference Δt.sub.2,3 between the second sensor 2 and the third sensor 3, a time difference Δt.sub.3,4 between the third sensor 3 and the fourth sensor 4, and a time difference Δt.sub.4,5 between the fourth sensor 4 and the fifth sensor 5 are sequentially obtained, and an average time difference Δt of the vehicle passing by two adjacent sensors in the five geomagnetic sensors is calculated as:

[00005]$\Delta t=\frac{1}{4}\underset{i=1}{\overset{4}{.Math.}}\Delta t_{i,i+1}.$

[0069] Step 7, a vehicle speed V is calculated.

[0070] A distance d between two adjacent sensors is obtained according to step 1 and an average time difference Δt between the two adjacent sensors after the vehicle passes by step 6, and a running speed of the vehicle is obtained by calculation:

[00006]$v=\frac{d}{\Delta t}.$

[0071] Step 8, a magnetic length of the vehicle when it passes by is calculated.

[0072] 8.1) durations Δt.sub.1, Δt.sub.2, . . . , Δt.sub.5 of the vehicle respectively passing by the five geomagnetic sensors are acquired according to the set threshold value of the magnetic field data of the arrival and departure of the vehicle and the recorded time stamp;

[0073] 8.2) an average duration Δt′ of the vehicle passing by each of the sensors is calculated:

Δt′=⅕(Δt.sub.1+Δt.sub.2+ . . . +Δt.sub.5)

[0074] 8.3) a magnetic length VML of a vehicle passing by is calculated according to a running speed v of the vehicle and an average duration Δt′ of the vehicle passing by each of the sensors:

VML=v×Δt′.

[0075] Step 9, a Z axis magnetic field strength threshold value is set and marked.

[0076] 9.1) Z axle magnetic field data of 5 geomagnetic sensors are acquired, and a Z axle magnetic field intensity threshold value is set as S, as shown in FIG. 5, S=−40 is set in the example; since magnetic field distribution under different road environments is different and different types of vehicles can generate different Z axis magnetic field waveforms when passing by, the threshold value S can be correspondingly set according to the geomagnetic waveforms obtained when different types of vehicles pass through an actual road, and only the Z axle magnetic field waveforms generated when different types of vehicles pass by are obviously differed;

[0077] 9.2) the geomagnetic baseline of the local magnetic field is respectively subtracting from the Z axial magnetic field data detected by the five geomagnetic sensors, the waveform data are recorded, whether or not at least one data is lower than a set threshold value S exists in the waveform data, if at least one data lower than the set threshold value S exists in the waveform data of the five geomagnetic sensors, it is marked as ‘1’, indicating that the vehicle might have been a large one and laying a basis for finally judging the types of vehicles; otherwise, it is marked as ‘0’.

[0078] Step 10, vehicle classification results are judged.

[0079] 10.1) double-threshold values L1 and L2 are set, and L1>L2;

[0080] Different types of vehicles will generate different vehicle magnetic lengths, i.e. the vehicles with particularly large vehicle lengths tend to generate larger vehicle magnetic lengths, which are generally referred to as large vehicles; the vehicles with particularly small vehicle lengths tend to generate smaller vehicle magnetic lengths, which are generally referred to as small vehicles; and the vehicles with medium vehicle lengths tend to generate medium vehicle magnetic lengths, so that it is difficult to distinguish whether the vehicle is a large one or a small one, and therefore the setting of the double threshold values L1 and L2 can be obtained by dividing the magnetic lengths of different types of vehicles passing by into different regions.

[0081] 10.2) the magnetic length VML of the vehicle when it passes by is compared with double-threshold values L1 and L2:

[0082] if the magnetic length of the vehicle when it passes by is VML≥L1, the vehicle is judged to be a large one;

[0083] if the magnetic length of the vehicle when it passes by is VML≤L2, the vehicle is judged to be a small one;

[0084] If the magnetic length of the vehicle when it passes by satisfies L2≤VML≤L1, a further judgment is made according to the Z axis magnetic field waveform thereof, i.e. whether or not the mark corresponding to the current vehicle is ‘1’ is searched, and if so, the vehicle is judged to be a large one; otherwise, to be a small one.

[0085] The example realizes an overall target of low power consumption, low cost, high reliability, easiness in realization and strong applicability, realizes the intelligent and information construction of the deployment area, is suitable for the construction of intelligent roads, and plays a significant role in assisting unmanned safety; according to the example, road vehicle type information can be accurately collected in real time by deploying the multiple geomagnetic sensors; in addition, an omnibearing management and control of the vehicles running on the road can be further realized through large-scale low-cost deployment of the geomagnetic sensors.

[0086] While the invention has been particularly shown and described with reference to a preferred embodiment thereof, it will be understood by a person skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims. The scope of the invention should, therefore, be determined with reference to the appended claims.