CN109147390A - A kind of Vehicle tracing method based on quantization adaptive Kalman filter - Google Patents
A kind of Vehicle tracing method based on quantization adaptive Kalman filter Download PDFInfo
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Abstract
A kind of car speed prediction technique based on quantization adaptive Kalman filter, the following steps are included: first, join in traffic system in intelligent network, the vehicle travelled by DSRC technology automatic identification simultaneously obtains related data, realizes the information exchange of onboard system and roadside unit；Then, for the relevant information of acquisition, quantified first by azimuth of the quantitative formula to roadside unit and onboard system, acceleration is predicted secondly by autoregressive moving average method, finally carry out horizontal position prediction using adaptive Kalman filter；Finally, the information handled well is sent to other 3 roadside units by optical cable, in order to next time with the information exchange of onboard system.The present invention provides a kind of Vehicle tracing methods based on quantization adaptive Kalman filter in the case where intelligent network joins traffic system.
Description
Technical field
The invention belongs to field of traffic, join one kind under traffic system more particularly, to intelligent network and are based on the adaptive card of quantization
The Vehicle tracing method of Kalman Filtering.
Background technique
The country most as world population of China, since reform and opening-up, with the rapid development of our country's economy, the people
The increasingly raising of living standard, private car initially enter every family, facilitate the trip of household well.But vehicle is universal
With popular but also urban traffic environment is worsening, there is unbalanced wagon flow, the collision of the congested in traffic, tailstock, side are touched
The traffic behaviors such as hit.Along with poor infrastructure and transportation network congestion, the quantity of road traffic accident increasingly increases, and height is handed over
Logical accident rate is sounded the alarm to the whole society, therefore traffic safety is greatly paid close attention to.In recent years, although I
State has carried out very big improvement to road infrastructure and transportation network, so that traffic accident quantity and the number of casualties are subtracted
It is few, but total number of accident and incidence are still very high.
Compared with traditional road traffic system, intelligent network connection traffic system be more intended to by " people ", " road ", " vehicle " with
And highway communication facility etc. carries out the dynamical system of information exchange.According to being found after a large amount of statistical research in various countries, driver's
Fault is to lead to the principal element of traffic accident.Therefore, it in the case where present road infrastructure cannot be improved again, obtains
By way of other lane vehicles of road status information and working process be broadcast to current vehicle work it is very urgent, this to drive
Member can preferably take corresponding remedial measure, reduce driver because of traffic accident caused by making mistakes.
Summary of the invention
The safety of existing road traffic system is lower, the higher deficiency of traffic accident probability of happening in order to overcome, this hair
It is bright to provide a kind of Vehicle tracing method based on quantization adaptive Kalman filter in the case where intelligent network joins traffic system.
The technical solution adopted by the present invention to solve the technical problems is:
A kind of Vehicle tracing method based on quantization adaptive Kalman filter, described method includes following steps:
1) joining in traffic system in intelligent network, the vehicle travelled by DSRC technology automatic identification simultaneously obtains related data,
Realize the information exchange of onboard system and roadside unit, wherein the step of information exchange is as follows:
Step 1.1: when driving vehicle enters in the range of directional aerial covers, onboard system can be logical with roadside unit
It crosses DSRC technology and realizes two-way communication, both sides is enabled to send the information in itself storage unit simultaneously, wherein onboard system hair
The information sent includes present speed, current location and the timestamp of vehicle, and the information that roadside unit is sent includes on other lanes
Which direction is the predicted position of vehicle be located on, a few lanes and acceleration；
Step 1.2: the information of vehicles that roadside unit will acquire is sent to edge Cloud Server and carries out a series of operation
Operation；
2) edge Cloud Server carries out azimuthal angle calculation and the amount of doing according to the gun parallax between roadside unit and onboard system
Change processing, quantifies vehicle heading for location information, wherein quantizing process are as follows:
Step 2.1: location information is converted into digital information existing for visual angle.Wherein, the actual bearer angle of vehicle
Relative to roadside unit is defined as:
Here, each parameter definition is as follows:
Azimuth between t moment roadside unit and onboard system；
θt: azimuthal antitrigonometric function is converted by the vehicle location of t moment；
σt: in t moment bearing error noise as caused by signal reflex；
(xt,yt): in the current location of t moment vehicle；
Step 2.2: using crossroad center as coordinate origin, to deflectionQuantification treatment is carried out, determines vehicle institute
Direction, wherein quantitative formula is as follows:
Here, each parameter definition is as follows:
bt: the quantized directions of t moment vehicle at the parting of the ways；
Q (): quantization function；
I: the direction signs of crossroad；
Step 2.3: the materialization in order to realize directional information quantifies the lane of the direction, by practical bearing angle
DegreeRenamed asIt is recorded as:
Here, each parameter definition is as follows:
Azimuth between t moment roadside unit and onboard system；
θt': the relative position of vehicle and roadside unit is converted azimuthal antitrigonometric function by t moment；
(xi,yi): the fixation position of roadside unit on the i of direction；
Step 2.4: being directed to above-mentioned quantitative formula, second is carried out to the direction where vehicle and is quantified, determine vehicle place
Lane；
Here, each parameter definition is as follows:
qt: the quantized directions in t moment lane；
ji: j-th of lane on the i of direction；
N: lane sum；
3) assume that p+1 nearest car speed, which is used only, carries out acceleration estimation, p-th of acceleration calculation mode are as follows:
Here, each parameter definition is as follows:
Δ τ: sampling time interval；
Δ v: the difference of the speed of later moment in time and previous moment；
vt-p: in the speed of t-p moment trolley；
τt-p: in the timestamp of t-p moment trolley；
at-p: p-th of acceleration value；
Hereafter, according to p acceleration value, vehicle acceleration prediction is carried out using autoregressive moving average method, wherein prediction
Formula is as follows:
Here, each parameter definition is as follows:
at: in the acceleration of t moment trolley；
P: Autoregressive, i.e. acceleration sum；
Q: moving average order, i.e. sliding sum；
β: the undetermined coefficient being not zero；
The undetermined coefficient being not zero；
ξt: in the independent error term of t moment；
4) assume that driver in the case where not lane change, for the relevant information of acquisition, utilizes adaptive Kalman filter
Algorithm carries out horizontal position prediction to driving vehicle, wherein the calculation formula of horizontal position are as follows:
State equation and observational equation are converted by above-mentioned formula using state-space model, wherein equation is as follows:
xt+1=Atxt+Btut+ωt；
zt=Ctxt+εt；
Here, each parameter definition is as follows:
xt: in the state vector of t moment trolley；
At,Bt,Ct: in the state-transition matrix of t moment；
ut: in the acceleration of t moment trolley；
ωt: in the systematic error of t moment, Gaussian distributed N (0, Qt), wherein QtFor in the process noise side of t moment
Difference；
εt: in the measurement error of t moment, Gaussian distributed N (0, Rt), wherein RtFor in the measurement noise side of t moment
Difference；
zt: in the State Viewpoint measured value of t moment system；
Hereafter, vehicle horizontal position is predicted using adaptive Kalman filter algorithm according to state-space model,
Wherein, the step of vehicle location updates is as follows:
Step 4.1: given initial valueWithWherein,Indicate the predicted value of t moment trolley horizontal position,Indicate t
The error covariance of moment trolley；
Step 4.2: according to given initial valueCalculate the kalman gain value K of t momentt, wherein
Step 4.3: according to the predicted value of t momentWith observation zt, the correction value of available current stateIts
In, formula is as follows:
zt=Ctxt+Rt
Step 4.4: updating error covarianceValue, for predict the t+1 moment error covariance prepare, whereinI is unit battle array；
Step 4.5: according to the correction value of t momentAnd acceleration utGo to the horizontal position of prediction t+1 moment trolleyWherein,
Step 4.6: predicting covariance, by the error covariance of previous momentGo the error of prediction later moment in time
CovarianceWherein,μt+1For adaptive forgetting factor；
Step 4.7: calculating the acceleration u at t+1 momentt+1, wherein calculation method such as it is described 3) in autoregression sliding it is flat
Equal method；
Step 4.8: updating the number of iterations k and be k=k+1 and come back to the calculating that step 4.2 starts a new round；
5) finally, edge Cloud Server sends the information handled well to roadside unit by optical cable, in order to next time
With the information exchange of onboard system.
Further, in the step 4.6, the calculation formula of adaptive forgetting factor are as follows:
μt+1=max { 1, tr (Nt+1)/tr(Mt+1)}；
Here, each parameter definition is as follows:
Nt+1: in the error variance at t+1 moment, it is ensured that its value is symmetrical and positive definite；
Lt+1: newly cease the covariance matrix inscribed in t+1；
et+1: in the difference of t+1 moment measured value and predicted value, i.e., new breath；
In the transposition of t+1 moment state-transition matrix C；
Mt+1: in the error variance at t+1 moment, it is ensured that error covarianceValue is symmetrical and positive definite；
Max { }: it is maximized more afterwards.
Further, in the step 1), in intelligent network connection traffic system, roadside unit installation at the parting of the ways red
On green light and side is accompanied with Cloud Server and directional aerial, wherein the launch angle of alignment antenna is 60 °, makes roadside unit
Can information exchange preferably be carried out with the onboard system in vehicle.
Further, in the step 1.2, it is contemplated that the memory capacity of edge Cloud Server is limited, so by server
In data be zeroed out every other week.
Technical concept of the invention are as follows: firstly, being travelled in intelligent network connection traffic system by DSRC technology automatic identification
Vehicle and obtain related data, realize onboard system and roadside unit information exchange.Then, believe for the correlation of acquisition
Breath, is quantified using azimuth of the quantitative formula to roadside unit and onboard system；Using autoregressive moving average method to adding
Speed is predicted；Horizontal position prediction is carried out using adaptive Kalman filter.Finally, the information handled well is passed through optical cable
Be sent to other 3 roadside units, in order to next time with the information exchange of onboard system.
Beneficial effects of the present invention are mainly manifested in: 1, by quantization to roadside unit and the gun parallax of onboard system,
Several lanes which direction current vehicle is located at can be apparent from.2, in conjunction with autoregressive moving average method and adaptive karr
Graceful filter method realizes the prediction of acceleration and position, and sends result to driver, so that driver can be according to vehicle
Relevant information and experience make suitable judgement and behavior, effectively reduce traffic accident rate.
Detailed description of the invention
Fig. 1 is the schematic diagram of mobile interchange traffic system information exchange.
Specific embodiment
Present invention is further described in detail with reference to the accompanying drawing.
Referring to Fig.1, a kind of Vehicle tracing method based on quantization adaptive Kalman filter, the present invention is based on one kind
Information Interaction Model (as shown in Figure 1) under DSRC technology communication.It is public by quantization first in intelligent network connection traffic system
Formula quantifies the azimuth of roadside unit and onboard system, carries out secondly by autoregressive moving average method to acceleration pre-
It surveys, finally carries out position prediction using adaptive Kalman filter；It the described method comprises the following steps:
1) joining in traffic system in intelligent network, the vehicle travelled by DSRC technology automatic identification simultaneously obtains related data,
Realize the information exchange of onboard system and roadside unit, wherein the step of information exchange is as follows:
Step 1.1: when driving vehicle enters in the range of directional aerial covers, onboard system can be logical with roadside unit
It crosses DSRC technology and realizes two-way communication, both sides is enabled to send the information in itself storage unit simultaneously, wherein onboard system hair
The information sent includes present speed, current location and the timestamp of vehicle, and the information that roadside unit is sent includes on other lanes
Which direction is the predicted position of vehicle be located on, a few lanes and acceleration；
Step 1.2: the information of vehicles that roadside unit will acquire is sent to edge Cloud Server and carries out a series of operation
Operation；
2) edge Cloud Server carries out azimuthal angle calculation and does phase according to the gun parallax between roadside unit and onboard system
The quantification treatment answered quantifies vehicle heading for location information, wherein quantizing process are as follows:
Step 2.1: location information is converted into digital information existing for visual angle, wherein the actual bearer angle of vehicle
Relative to roadside unit is defined as:
Here, each parameter definition is as follows:
Azimuth between t moment roadside unit and onboard system；
θt: azimuthal antitrigonometric function is converted by the vehicle location of t moment；
σt: in t moment bearing error noise as caused by signal reflex；
(xt,yt): in the current location of t moment vehicle；
Step 2.2: using crossroad center as coordinate origin, to deflectionQuantification treatment is carried out, determines vehicle institute
Direction, wherein quantitative formula is as follows:
Here, each parameter definition is as follows:
bt: the quantized directions of t moment vehicle at the parting of the ways；
Q (): quantization function；
I: the direction signs of crossroad；
Step 2.3: the materialization in order to realize directional information quantifies the lane of the direction.By practical bearing angle
DegreeRenamed asIt is recorded as:
Here, each parameter definition is as follows:
Azimuth between t moment roadside unit and onboard system；
θt': the relative position of vehicle and roadside unit is converted azimuthal antitrigonometric function by t moment；
(xi,yi): the fixation position of roadside unit on the i of direction；
Step 2.4: being directed to above-mentioned quantitative formula, second is carried out to the direction where vehicle and is quantified, determine vehicle place
Lane；
Here, each parameter definition is as follows:
qt: the quantized directions in t moment lane；
ji: j-th of lane on the i of direction；
N: lane sum；
3) assume that p+1 nearest car speed, which is used only, carries out acceleration estimation, p-th of acceleration calculation mode are as follows:
Here, each parameter definition is as follows:
Δ τ: sampling time interval；
Δ v: the difference of the speed of later moment in time and previous moment；
vt-p: in the speed of t-p moment trolley；
τt-p: in the timestamp of t-p moment trolley；
at-p: p-th of acceleration value；
Hereafter, according to p acceleration value, vehicle acceleration prediction is carried out using autoregressive moving average method, wherein prediction
Formula is as follows:
Here, each parameter definition is as follows:
at: in the acceleration of t moment trolley；
P: Autoregressive, i.e. acceleration sum；
Q: moving average order, i.e. sliding sum；
β: the undetermined coefficient being not zero；
The undetermined coefficient being not zero；
ξt: in the independent error term of t moment；
4) assume that driver in the case where not lane change, for the relevant information of acquisition, utilizes adaptive Kalman filter
Algorithm carries out horizontal position prediction to driving vehicle, wherein the calculation formula of horizontal position are as follows:
State equation and observational equation are converted by above-mentioned formula using state-space model, wherein equation is as follows:
xt+1=Atxt+Btut+ωt；
zt=Ctxt+εt；
Here, each parameter definition is as follows:
xt: in the state vector of t moment trolley；
At,Bt,Ct: in the state-transition matrix of t moment；
ut: in the acceleration of t moment trolley；
ωt: in the systematic error of t moment, Gaussian distributed N (0, Qt), wherein QtFor in the process noise side of t moment
Difference；
εt: in the measurement error of t moment, Gaussian distributed N (0, Rt), wherein RtFor in the measurement noise side of t moment
Difference；
zt: in the State Viewpoint measured value of t moment system；
Hereafter, vehicle horizontal position is predicted using adaptive Kalman filter algorithm according to state-space model,
Wherein, the step of vehicle location updates is as follows:
Step 4.1: given initial valueWithWherein,Indicate the predicted value of t moment trolley horizontal position,It indicates
The error covariance of t moment trolley；
Step 4.2: according to given initial valueCalculate the kalman gain value K of t momentt, wherein
Step 4.3: according to the predicted value of t momentWith observation zt, the correction value of available current stateIts
In, formula is as follows:
zt=Ctxt+Rt
Step 4.4: updating error covarianceValue, for predict the t+1 moment error covariance prepare, whereinI is unit battle array；
Step 4.5: according to the correction value of t momentAnd acceleration utGo to the horizontal position of prediction t+1 moment trolleyWherein,
Step 4.6: predicting covariance, by the error covariance of previous momentGo to the error association of prediction later moment in time
VarianceWherein,μt+1For adaptive forgetting factor；
Step 4.7: calculating the acceleration u at t+1 momentt+1, wherein calculation method such as it is described 3) in autoregression sliding it is flat
Equal method；
Step 4.8: updating the number of iterations k and be k=k+1 and come back to the calculating that step 4.2 starts a new round；
5) finally, edge Cloud Server sends the information handled well to roadside unit by optical cable, in order to next time
With the information exchange of onboard system.
Further, in the step 4.6, the calculation formula of adaptive forgetting factor are as follows:
μt+1=max { 1, tr (Nt+1)/tr(Mt+1)}；
Here, each parameter definition is as follows:
Nt+1: in the error variance at t+1 moment, it is ensured that its value is symmetrical and positive definite；
Lt+1: newly cease the covariance matrix inscribed in t+1；
et+1: in the difference of t+1 moment measured value and predicted value, i.e., new breath；
In the transposition of t+1 moment state-transition matrix C；
Mt+1: in the error variance at t+1 moment, it is ensured that error covarianceValue is symmetrical and positive definite；
Max { }: it is maximized more afterwards.
Further, in the step 1), in intelligent network connection traffic system, roadside unit installation at the parting of the ways red
On green light and side is accompanied with Cloud Server and directional aerial, wherein the launch angle of alignment antenna is 60 °, makes roadside unit
Can information exchange preferably be carried out with the onboard system in vehicle.
Further, in the step 1.2, it is contemplated that the memory capacity of edge Cloud Server is limited, so by server
In data be zeroed out every other week.
Claims (4)
1. a kind of Vehicle tracing method based on quantization adaptive Kalman filter, which is characterized in that the method includes
Following steps:
1) join in traffic system in intelligent network, the vehicle travelled by DSRC technology automatic identification simultaneously obtains related data, realizes
The information exchange of onboard system and roadside unit, wherein the step of information exchange is as follows:
Step 1.1: when driving vehicle enters in the range of directional aerial covers, onboard system can pass through with roadside unit
DSRC technology realizes two-way communication, and both sides is enabled to send the information in itself storage unit simultaneously, wherein onboard system is sent
Information include vehicle present speed, current location and timestamp, roadside unit send information include that other lanes are got on the bus
Predicted position, be located on which direction, a few lanes and acceleration；
Step 1.2: the information of vehicles that roadside unit will acquire is sent to edge Cloud Server and carries out a series of arithmetic operation；
2) edge Cloud Server carries out azimuthal angle calculation and does corresponding according to the gun parallax between roadside unit and onboard system
Quantification treatment quantifies vehicle heading for location information, wherein quantizing process are as follows:
Step 2.1: location information is converted into digital information existing for visual angle, wherein the actual bearer angle of vehicleRelatively
In roadside unit is defined as:
Here, each parameter definition is as follows:
Azimuth between t moment roadside unit and onboard system；
θt: azimuthal antitrigonometric function is converted by the vehicle location of t moment；
σt: in t moment bearing error noise as caused by signal reflex；
(xt,yt): in the current location of t moment vehicle；
Step 2.2: using crossroad center as coordinate origin, to deflectionQuantification treatment is carried out, where determining vehicle
Direction, wherein quantitative formula is as follows:
Here, each parameter definition is as follows:
bt: the quantized directions of t moment vehicle at the parting of the ways；
Q (): quantization function；
I: the direction signs of crossroad；
Step 2.3: the materialization in order to realize directional information quantifies the lane of the direction, by practical bearing angleWeight
It is named asIt is recorded as:
Here, each parameter definition is as follows:
Azimuth between t moment roadside unit and onboard system；
θt': the relative position of vehicle and roadside unit is converted azimuthal antitrigonometric function by t moment；
(xi,yi): the fixation position of roadside unit on the i of direction；
Step 2.4: being directed to above-mentioned quantitative formula, second is carried out to the direction where vehicle and is quantified, determines the vehicle where vehicle
Road；
Here, each parameter definition is as follows:
qt: the quantized directions in t moment lane；
ji: j-th of lane on the i of direction；
N: lane sum；
3) assume that p+1 nearest car speed, which is used only, carries out acceleration estimation, p-th of acceleration calculation mode are as follows:
Here, each parameter definition is as follows:
Δ τ: sampling time interval；
Δ v: the difference of the speed of later moment in time and previous moment；
vt-p: in the speed of t-p moment trolley；
τt-p: in the timestamp of t-p moment trolley；
at-p: p-th of acceleration value；
Hereafter, according to p acceleration value, vehicle acceleration prediction is carried out using autoregressive moving average method, wherein predictor formula
It is as follows:
Here, each parameter definition is as follows:
at: in the acceleration of t moment trolley；
P: Autoregressive, i.e. acceleration sum；
Q: moving average order, i.e. sliding sum；
β: the undetermined coefficient being not zero；
The undetermined coefficient being not zero；
ξt: in the independent error term of t moment；
4) assume that driver in the case where not lane change, for the relevant information of acquisition, utilizes adaptive Kalman filter algorithm
Horizontal position prediction is carried out to driving vehicle, wherein the calculation formula of horizontal position are as follows:
State equation and observational equation are converted by above-mentioned formula using state-space model, wherein equation is as follows:
xt+1=Atxt+Btut+ωt；
zt=Ctxt+εt；
Here, each parameter definition is as follows:
xt: in the state vector of t moment trolley；
At,Bt,Ct: in the state-transition matrix of t moment；
ut: in the acceleration of t moment trolley；
ωt: in the systematic error of t moment, Gaussian distributed N (0, Qt), wherein QtFor in the process-noise variance of t moment；
εt: in the measurement error of t moment, Gaussian distributed N (0, Rt), wherein RtFor in the measurement noise variance of t moment；
zt: in the State Viewpoint measured value of t moment system；
Hereafter, vehicle horizontal position is predicted using adaptive Kalman filter algorithm according to state-space model, wherein
The step of vehicle location updates is as follows:
Step 4.1: given initial valueWithWherein,Indicate the predicted value of t moment trolley horizontal position,Indicate t moment
The error covariance of trolley；
Step 4.2: according to given initial valueCalculate the kalman gain value K of t momentt, wherein
Step 4.3: according to the predicted value of t momentWith observation zt, the correction value of available current stateWherein, public
Formula is as follows:
zt=Ctxt+Rt
Step 4.4: updating error covarianceValue, for predict the t+1 moment error covariance prepare, whereinI is unit battle array；
Step 4.5: according to the correction value of t momentAnd acceleration utGo to the horizontal position of prediction t+1 moment trolleyIts
In,
Step 4.6: predicting covariance, by the error covariance of previous momentRemove the error covariance of prediction later moment in timeWherein,μt+1For adaptive forgetting factor；
Step 4.7: calculating the acceleration u at t+1 momentt+1, wherein calculation method it is as described in claim 1 3) in autoregression it is sliding
The dynamic method of average；
Step 4.8: updating the number of iterations k and be k=k+1 and come back to the calculating that step 4.2 starts a new round；
5) finally, edge Cloud Server sends the information handled well to roadside unit by optical cable, in order to next time with vehicle
The information exchange of loading system.
2. a kind of Vehicle tracing method based on quantization adaptive Kalman filter as described in claim 1, feature
It is: in the step 1), in intelligent network connection traffic system, on the traffic lights of roadside unit installation at the parting of the ways and side
It is accompanied with edge Cloud Server and directional aerial, wherein the launch angle of alignment antenna is 60 °, keeps roadside unit better
Information exchange is carried out with the onboard system in vehicle.
3. a kind of Vehicle tracing method based on quantization adaptive Kalman filter as claimed in claim 1 or 2, special
Sign is: in the step 4.6, the calculation formula of adaptive forgetting factor are as follows:
μt+1=max { 1, tr (Nt+1)/tr(Mt+1)}；
Here, each parameter definition is as follows:
Nt+1: in the error variance at t+1 moment, it is ensured that its value is symmetrical and positive definite；
Lt+1: newly cease the covariance matrix inscribed in t+1；
et+1: in the difference of t+1 moment measured value and predicted value, i.e., new breath；
In the transposition of t+1 moment state-transition matrix C；
Mt+1: in the error variance at t+1 moment, it is ensured that error covarianceValue is symmetrical and positive definite；
Max { }: it is maximized more afterwards.
4. a kind of Vehicle tracing method based on quantization adaptive Kalman filter as claimed in claim 1 or 2, special
Sign is: in the step 1.2, it is contemplated that the memory capacity of edge Cloud Server is limited, so the data in server are every
It was zeroed out every one week.
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| CN110264721A (en) * | 2019-07-01 | 2019-09-20 | 北京理工大学 | A kind of urban intersection nearby vehicle trajectory predictions method |
| CN110728309A (en) * | 2019-09-27 | 2020-01-24 | 中国铁道科学研究院集团有限公司通信信号研究所 | Traffic track clustering method based on railway signals and Beidou positioning |
| CN112051569A (en) * | 2020-09-10 | 2020-12-08 | 北京润科通用技术有限公司 | Radar target tracking speed correction method and device |
| CN113115230A (en) * | 2021-03-26 | 2021-07-13 | 北京工业大学 | Vehicle broadcast communication control method based on information physical system |
| CN114034298A (en) * | 2021-11-04 | 2022-02-11 | 吉林大学 | Vehicle positioning method based on reconfigurable intelligent surface |
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