CN111308458B - Vehicle speed estimation method based on vehicle millimeter wave radar - Google Patents
Vehicle speed estimation method based on vehicle millimeter wave radar Download PDFInfo
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- CN111308458B CN111308458B CN202010108361.3A CN202010108361A CN111308458B CN 111308458 B CN111308458 B CN 111308458B CN 202010108361 A CN202010108361 A CN 202010108361A CN 111308458 B CN111308458 B CN 111308458B
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/02—Systems using reflection of radio waves, e.g. primary radar systems; Analogous systems
- G01S13/50—Systems of measurement based on relative movement of target
- G01S13/58—Velocity or trajectory determination systems; Sense-of-movement determination systems
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
- G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
- G01S7/418—Theoretical aspects
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Abstract
The invention discloses a vehicle speed estimation method based on a vehicle millimeter wave radar, which requires that the radar is arranged at a forward horizontal position of a vehicle to be detected, and a known fixed installation angle exists between the main transmitting beam direction of the radar and the central axis direction of the vehicle to be detected. The method comprises the following steps: receiving a reflected wave signal from a forward target by a vehicle-mounted high-precision millimeter wave radar, obtaining relative speed information of the target in a field of view by a radar signal processing technology, and updating a vehicle speed estimation result at intervals; performing associated filtering processing on all speed detection results in a single updating period, and matching confidence coefficient with each self-vehicle speed estimation result; and performing smoothing filtering processing on the self-vehicle speed estimation result in a plurality of updating periods to obtain the self-vehicle speed estimation result with corresponding confidence. The invention has the advantages of high real-time performance, high precision and strong universality.
Description
Technical Field
The invention belongs to the technical field of vehicle speed estimation, and particularly relates to a vehicle speed estimation method based on a vehicle millimeter wave radar.
Background
With the development of intelligent traffic systems, people are continuously raising the attention of safety driving of automobiles. In the running process of the vehicle, accurate sensing of the speed of the vehicle can help a driver to control and adjust the running state of the vehicle, and is an important step for realizing safe driving. Therefore, as important information in the active safety system of the vehicle, real-time and accurate estimation of the longitudinal speed of the vehicle becomes a problem to be solved urgently.
In the prior art, an OBD interface is adopted to access a vehicle internal bus system through a vehicle-mounted terminal with satellite positioning and mobile communication functions installed on a vehicle, so as to acquire state data of the vehicle. The OBD speed information of the vehicle is acquired at a fixed time interval delta T through the OBD bus, and good measurement accuracy is obtained. However, the design has the disadvantage that the time interval delta T is limited to be larger in the process of acquiring the vehicle speed, and the vehicle speed estimation result cannot be output in real time.
Disclosure of Invention
In view of the above, the invention aims to provide a vehicle speed estimation method based on a vehicle millimeter wave radar, which can realize real-time accurate estimation of the vehicle speed of a motor vehicle.
A vehicle speed estimation method based on a vehicle millimeter wave radar is characterized by comprising the following steps:
s1: the vehicle to be tested stably runs along the highway at any speed, and a 77GHz vehicle-mounted high-precision millimeter wave radar emits a periodic FMCW waveform;
s2: the radar receives echo signals, and carries out two-dimensional FFT on single-frame echo data to obtain R-D spectrum;
s3: calculating the distance, radial speed and azimuth angle of each target;
s4: calculating the relative speed of the target and the normal direction of the vehicle to be tested:
wherein V is r And θ is the target radial velocity and target azimuth angle obtained in step S3, respectively; beta is the radar installation angle;
s5: counting the target group velocity information by utilizing a velocity discrimination algorithm, classifying targets by setting a velocity error threshold Verror, screening out moving targets, and performing confidence matching to obtain a target group result with the top three ranks of the target numbers;
firstly, sorting targets according to the speed of the targets;
secondly, dividing target speeds with speed errors within Verror into a cluster based on a set speed error threshold Verror;
thirdly, estimating the average value of the speeds in each cluster of targets;
finally, setting the speed estimation confidence according to the number of targets in each cluster, wherein the speed estimation confidence of each cluster is higher as the number of targets is larger; reserving the top three clusters of the target number in the clusters;
s6: according to the set radar measurement target speed range, eliminating target clusters exceeding the speed boundary in the clusters with the top three ranks of the target numbers;
s7: self-vehicle speed estimation:
firstly, judging: if the target clusters have the target clusters with the speed estimation confidence coefficient higher than the set threshold, the speed average value estimation result of the target clusters meeting the conditions is directly output as the speed estimation result of the own vehicle;
if the target clusters meeting the conditions are not available, calculating the error between the current speed estimated value of each target cluster and the estimated own vehicle speed of the previous two frames, and outputting the speed average value of the target cluster with the minimum error as an own vehicle speed estimated result;
when the conditions are not matched, the average speed of the target cluster with the largest number of targets is selected to be used as the speed estimation result of the vehicle.
In the step, the smoothing filter processing sets a sliding window with a fixed length, and the sliding window slides backwards along with the delay window of the frame number, so that the data in the window is smoothed and filtered, and the speed estimation error of the vehicle can be reduced.
Further, after the multi-frame self-vehicle speed estimation is completed, smoothing filtering processing is carried out on the adjacent multi-frame self-vehicle speed estimation results, and finally the self-vehicle speed estimation results are determined.
Preferably, in the step S3, the velocity of the target can be calculated according to the R-D spectrum doppler dimension result, and the formula is as follows:
wherein T is c For the time interval of adjacent emission wave signals, lambda is the corresponding wavelength of the initial frequency of the emission wave, and omega is the phase interval of two continuous emission wave signals;
the angle of the target is calculated according to the following formula:
where Δd is the distance between adjacent receiving antennas, and λ is the wavelength corresponding to the starting frequency of the transmitting wave.
The invention has the following beneficial effects:
the invention discloses a vehicle speed estimation method based on a vehicle millimeter wave radar, which requires that the radar is arranged at a forward horizontal position of a vehicle to be detected, and a known fixed installation angle exists between the main transmitting beam direction of the radar and the central axis direction of the vehicle to be detected. The method comprises the following steps: receiving a reflected wave signal from a forward target by a vehicle-mounted high-precision millimeter wave radar, obtaining relative speed information of the target in a field of view by a radar signal processing technology, and updating a vehicle speed estimation result at intervals; performing associated filtering processing on all speed detection results in a single updating period, and matching confidence coefficient with each self-vehicle speed estimation result; and performing smoothing filtering processing on the self-vehicle speed estimation result in a plurality of updating periods to obtain the self-vehicle speed estimation result with corresponding confidence. The invention has the advantages of high real-time performance, high precision and strong universality.
Drawings
FIG. 1 is a schematic diagram of an apparatus for a vehicle-mounted radar vehicle speed estimation method of the present invention;
fig. 2 is a schematic diagram of a processing result of a vehicle radar self-vehicle speed estimation method.
Detailed Description
The invention will now be described in detail by way of example with reference to the accompanying drawings.
In fig. 1, the radar is installed in front of a car, the car starts to run after the radar coordinate system is aligned with the car coordinate system, M2 and M8 are radar detection moving target results, and M1, M3, M4, M5, M6 and M7 are radar detection static target results. According to the target speed screening algorithm, the speed of the static target is obtained, and the speed estimation of the self-vehicle is further obtained according to the geometric relation, and the specific steps are as follows:
s1: the vehicle to be tested stably runs along the highway at any speed, and a 77GHz vehicle-mounted high-precision millimeter wave radar emits a periodic FMCW waveform;
s2: the radar receives echo signals, and carries out two-dimensional FFT on single-frame echo data to obtain R-D spectrum; in this step, a single frame includes pulses of N antenna receive channels, each receive channel including M pulse repetition periods, each pulse repetition period including P sampling points.
S3: obtaining an R-D spectrum by utilizing each frame of echo, calculating the distance and the radial speed of each target, and then performing fft based on the multi-channel data of the target point to obtain an angle dimension one-dimensional image, thereby obtaining the azimuth angle of each target;
in this step, the distance of the target in the field of view is calculated according to the following formula:
wherein: d is the target distance, f IF For the target intermediate frequency signal frequency, c is the speed of light, and S is the frequency modulation slope of the radar transmit chirp signal.
The velocity of the target can be calculated from the R-D spectral Doppler results, and the basic formula is as follows:
wherein T is c For the time interval between adjacent transmission wave signals, lambda is the corresponding wavelength of the transmission wave starting frequency, and omega is the phase interval between two continuous transmission wave signals.
The angle of the target is calculated according to the following formula:
where Δd is the distance between adjacent receiving antennas, and λ is the wavelength corresponding to the starting frequency of the transmitting wave.
S4: calculating the relative speed of the target group and the normal direction of the vehicle to be tested by using the known radar mounting angle; the radar installation angle refers to an offset angle of the radar main beam center in a horizontal plane relative to the normal direction of the vehicle in a vehicle body coordinate system;
in this step, the following formula is used to calculate the relative speed between the target and the normal direction of the vehicle to be measured:
wherein: v (V) r The target radial velocity measured for the radar, θ is the target azimuth angle measured for the radar, β is the radar mounting angle.
S5: counting the target group velocity information by utilizing a velocity discrimination algorithm, classifying targets by setting a velocity error threshold Verror, screening out moving targets, and performing confidence matching to obtain a target group result with the top three ranks of the target numbers;
in the step, the step of screening out the moving target is as follows: firstly, sorting the speed results of the detected targets; then clustering the detection speed results according to a speed error threshold Verror, dividing the speeds with the speed error within the Verror into a cluster until all the detection speeds are clustered, and carrying out average value estimation on the detection speed results of each cluster; and finally, carrying out confidence degree classification according to the number of targets in each cluster, wherein the speed estimation confidence degree of each cluster is higher as the number of targets is larger. Here, the confidence indicates the degree to which this velocity estimation result is believed.
S6: defining a radar measurement target speed range, and eliminating target groups exceeding a speed boundary;
s7: preliminary obtaining a self-vehicle speed estimation result of the current frame by using a confidence judging and multi-frame data association algorithm;
in this step, the step of estimating the vehicle speed is: firstly, judging confidence coefficient preferentially, and outputting a speed average value estimation result with the confidence coefficient higher than a certain threshold value as a self-vehicle speed estimation result directly; secondly, judging the situation that the confidence judging condition is not met by using a multi-frame data association method, wherein the judging method is to carry out threshold association with the speed result of the last two frames of self-vehicle, and the association is considered to be successful according to the threshold requirement, and a group with the minimum association error is selected to be output as the speed estimation result of the self-vehicle; and when the two conditions are not matched, selecting the average speed result with the largest number in the target cluster as the speed estimation result of the vehicle to output.
S8: and carrying out smoothing filtering processing on the adjacent multi-frame self-vehicle speed estimation results, and finally determining the self-vehicle speed estimation results.
In the step, the smoothing filter processing sets a sliding window with a fixed length, and the sliding window slides backwards along with the delay window of the frame number, so that the data in the window is smoothed and filtered, and the speed estimation error of the vehicle can be reduced.
The result of the millimeter wave radar self-speed estimation, the comparison of the millimeter wave radar self-speed estimation and the OBD output vehicle speed is shown in fig. 2. The vehicle-mounted radar vehicle speed estimation method outputs vehicle speed in real time, and the OBD outputs vehicle speed at a certain time interval; compared with the OBD output vehicle speed, the millimeter wave radar estimation result has high precision and the error is less than 0.4m/s. In summary, the above embodiments are only preferred embodiments of the present invention, and are not intended to limit the scope of the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (3)
1. A vehicle speed estimation method based on a vehicle millimeter wave radar is characterized by comprising the following steps:
s1: the vehicle to be tested stably runs along the highway at any speed, and a 77GHz vehicle-mounted high-precision millimeter wave radar emits a periodic FMCW waveform;
s2: the radar receives echo signals, and carries out two-dimensional FFT on single-frame echo data to obtain R-D spectrum;
s3: calculating the distance, radial speed and azimuth angle of each target;
s4: calculating the relative speed of the target and the normal direction of the vehicle to be tested:
wherein V is r And θ is the target radial velocity and target azimuth angle obtained in step S3, respectively; beta is the radar installation angle;
s5: counting the target group velocity information by utilizing a velocity discrimination algorithm, classifying targets by setting a velocity error threshold Verror, screening out moving targets, and performing confidence matching to obtain a target group result with the top three ranks of the target numbers;
firstly, sorting targets according to the speed of the targets;
secondly, dividing target speeds with speed errors within Verror into a cluster based on a set speed error threshold Verror;
thirdly, estimating the average value of the speeds in each cluster of targets;
finally, setting the speed estimation confidence according to the number of targets in each cluster, wherein the speed estimation confidence of each cluster is higher as the number of targets is larger; reserving the top three clusters of the target number in the clusters;
s6: according to the set radar measurement target speed range, eliminating target clusters exceeding the speed boundary in the clusters with the top three ranks of the target numbers;
s7: self-vehicle speed estimation:
firstly, judging: if the target clusters have the target clusters with the speed estimation confidence coefficient higher than the set threshold, the speed average value estimation result of the target clusters meeting the conditions is directly output as the speed estimation result of the own vehicle;
if the target clusters meeting the conditions are not available, calculating the error between the current speed estimated value of each target cluster and the estimated own vehicle speed of the previous two frames, and outputting the speed average value of the target cluster with the minimum error as an own vehicle speed estimated result;
when the conditions are not consistent, selecting the average speed of the target cluster with the largest number of targets as a vehicle speed estimation result and outputting the average speed;
in the step, the smoothing filter processing sets a sliding window with a fixed length, and the sliding window slides backwards along with the delay window of the frame number, so that the data in the window is smoothed and filtered, and the speed estimation error of the vehicle can be reduced.
2. The vehicle speed estimation method based on the vehicle millimeter wave radar according to claim 1, wherein after the vehicle speed estimation of a plurality of frames is completed, smoothing filter processing is performed on the vehicle speed estimation results of adjacent frames, and the vehicle speed estimation results are finally determined.
3. The vehicle speed estimation method based on the vehicle millimeter wave radar according to claim 1, wherein in the step S3, the speed of the target can be calculated according to the R-D spectrum doppler dimension result, and the formula is as follows:
wherein T is c For the time interval of adjacent emission wave signals, lambda is the corresponding wavelength of the initial frequency of the emission wave, and omega is the phase interval of two continuous emission wave signals;
the angle of the target is calculated according to the following formula:
where Δd is the distance between adjacent receiving antennas, and λ is the wavelength corresponding to the starting frequency of the transmitting wave.
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| EP4163671A4 (en) * | 2020-06-24 | 2023-08-09 | Huawei Technologies Co., Ltd. | Target detection method and apparatus, radar, and vehicle |
| CN111751817A (en) * | 2020-07-21 | 2020-10-09 | 成都阶跃时进科技有限公司 | Vehicle driving parameter measuring device and method |
| CN112731317B (en) * | 2020-12-25 | 2024-03-19 | 北京百度网讯科技有限公司 | Vehicle radar calibration method, device, equipment, medium and automatic driving vehicle |
| CN112785854B (en) * | 2021-01-11 | 2022-09-16 | 北京百度网讯科技有限公司 | Vehicle speed detection method, device, equipment, medium and automatic driving vehicle |
| CN113256990B (en) * | 2021-07-13 | 2021-09-28 | 北京戍宁信息技术有限公司 | Method and system for collecting road vehicle information by radar based on clustering algorithm |
| CN115291203A (en) * | 2022-10-08 | 2022-11-04 | 济南嘉宏科技有限责任公司 | Speed processing method of mine speed measuring radar |
| CN117783572A (en) * | 2023-12-20 | 2024-03-29 | 苏州承泰科技有限公司 | Vehicle speed estimation method, system and platform suitable for multiple scenes |
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